Traveling

Through

Time 

The Forum Guide to 

Longitudinal Data Systems 

Effectively Managing LDS Data 

National Cooperative Education Statistics System
 

The National Center for Education Statistics established the National Cooperative Education Statistics System 

(Cooperative System) to assist in producing and maintaining comparable and uniform information and data on early 

childhood education and elementary and secondary education. These data are intended to be useful for policymaking at 

the federal, state, and local levels. 

The National Forum on Education Statistics (the Forum), among other activities, proposes principles of good practice to 

assist state and local education agencies in meeting this purpose. The Cooperative System and the Forum are supported 

in these endeavors by resources from the National Center for Education Statistics (NCES). 

Publications of the Forum do not undergo the formal review required for products of NCES. The information and 

opinions published here are those of the Forum and do not necessarily represent the policy or views of the U.S. 

Department of Education or the National Center for Education Statistics. 

February 2011 

This publication and other publications of the Forum may be found at the NCES websites. 

The NCES World Wide Web Home Page is http://nces.ed.gov 

The NCES World Wide Web Electronic Catalog is http://nces.ed.gov/pubsearch 

The Forum World Wide Web Home Page is http://nces.ed.gov/forum 

This publication was prepared in part under contract number ED-06-CO-0056 with the Council of Chief State 

School Officers. Mention of trade names, commercial products, or organizations does not imply endorsement by 

the U.S. Government. 

Suggested Citation: 

National Forum on Education Statistics. (2011). Traveling Through Time: The Forum Guide to Longitudinal Data Systems. 

Book Three of Four: Effectively Managing LDS Data (NFES 2011–805). Washington, DC: National Center for Education 

Statistics, Institute of Education Sciences, U.S. Department of Education. 

For ordering information on this report, write: 

ED Pubs 

U.S. Department of Education 

P.O. Box 22207 

Alexandria, VA 22304 

Or call toll free 1–877–4ED–PUBS; or order online at http://www.edpubs.gov 

Technical Contact: 

Tate Gould 

202–219–7080 

tate.gould@ed.gov 

ii Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
The longitudinal Data 

Systems Task Force 

A volunteer task force of the National Forum on Education Statistics (the Forum) 

produced this document. It was developed through the National Cooperative Education 

Statistics System and funded by the National Center for Education Statistics (NCES) of 

the U.S. Department of Education. 

Chair 

Bruce E. Dacey 

Delaware Department of Education 

Dover, Delaware 

Task Force Members 

Nancy Burke Nancy J. Smith 

Century Elementary School (formerly of the National Center 

Grafton Public School District for Education Statistics) 

Grafton, North Dakota DataSmith Solutions, LLC 

Vienna, Virginia 

Corey Chatis 

(formerly of the Tennessee Department Steve Smith 

of Education) Cambridge Public School District 

Bill and Melinda Gates Foundation Cambridge, Massachusetts 

Seattle, Washington 

Raymond Yeagley 

Laurie Collins Northwest Evaluation Association 

(formerly of the SIF Association) Lake Oswego, Oregon 

Mizuni, Inc. 

Addison, Texas 

Patrick Sherrill 

Office of Planning, Evaluation, 

and Policy Development 

U.S. Department of Education 

Washington, DC 

Consulting Author 

Anthony Garofano 

Quality Information Partners 

Fairfax, Virginia 

Project Officer 

Tate Gould 

National Center for Education Statistics 

Washington, DC 

Book Three of Four: Effectively Managing LDS Data iii 
acknowledgments
 

The members of the Longitudinal Data Systems Task Force of the National Forum on 

Education Statistics would like to thank everyone who reviewed drafts of this document or 

otherwise contributed to its development. This includes the Forum Steering Committee and 

Technology (TECH) Committee and the members of the National Forum on Education 

Statistics. The Task Force wishes to specifically acknowledge the contributions of 

Lee Hoffman Sharon Lewellyn and Patricia Sullivan 

formerly of the National Center for Texas Education Agency 

Education Statistics 

Beth Juillerat and Marsha Ward 

Maureen Wentworth Ohio Department of Education 

Council of Chief State School Officers 

Susan VanGorden 

Kathy Gosa and Ted Carter Lakota Local School District, Ohio 

Kansas State Department of Education 

Mark Russell 

Donald Houde formerly of the Idaho State Department of 

Arizona Department of Education Education 

Larry Fruth and Jim Campbell Khaled Falah and Tom Cosgrove 

Schools Interoperability formerly of the Washington, DC Office of 

Framework Association the State Superintendent of Education 

Hugh Walkup Robert McGlaughlin 

U.S. Department of Education New Hampshire Department of Education 

Barbara Clements 

ESP Solutions 

The Task Force also thanks eScholar and several of its clients (Pennsylvania Department 

of Education, Nebraska Department of Education, and New Mexico Public Education 

Department) for their insights and suggestions to improve this product. Frances Erlebacher 

provided overall editing and the Creative Shop formatted and designed this publication. 

iv Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Book Three of Four: Effectively Managing LDS Data v

Foreword

The National Forum on Education Statistics (the Forum) is pleased to present Traveling 

Through Time: The Forum Guide to Longitudinal Data Systems. This document, Book Three 

of Four: Effectively Managing LDS Data, is the third installment of this Forum series of 

guides on longitudinal data systems (LDS). One goal of the Forum is to improve the 

quality of education data gathered for use by policymakers and program decisionmakers. 

An approach to furthering this goal has been to pool the collective experiences of Forum 

members to produce “best practice” guides in areas of high interest to those who collect, 

maintain, and use data about elementary and secondary education. Developing LDSs 

is one of those high-interest areas. These systems hold promise for enhancing both the 

way education agencies use data to serve students and the way they do business, from the 

policy level to the school office and into the classroom.

LDSs are increasingly becoming the state of the art in education data. These systems 

move us from relying on blunt, aggregate, snapshot student data; to detailed and timely, 

student-level data that reflect the student’s entire academic history. An LDS makes it 

possible to not only monitor the success of individual students, but also to identify trends 

in those students’ education records. Freeing educators from guesswork and lessening the 

burden of painstaking data analysis, these systems provide powerful and timely insight 

about students and allow educators to tailor instruction to better meet individual needs. 

An LDS can reveal with great clarity what effects our policies, programs, and decisions have 

on schools. These systems allow agencies to track students across institutions to facilitate 

appropriate course placement and to determine who has transferred and who has dropped 

out. Longitudinal data systems also offer a new level of sophistication at the business 

level that can streamline operations; improve data quality; and free up valuable resources 

previously allocated to inefficient data entry, maintenance, and reporting practices.

For these and others reasons, states should continue to introduce, develop, and 

expand their LDSs. The Traveling Through Time: The Forum Guide to Longitudinal Data 

Systems series is intended to help state and local education agencies meet the many 

challenges involved in developing robust systems, populating them with quality data, and 

using this new information to improve the education system. The series will introduce 

important topics, offer best practices, and direct the reader to additional resources related 

to the LDS development process. In sum, it is intended to help agencies establish LDSs 

that will have lasting, far-reaching impact on the education system and on students’ lives. 

For a description of the entire guide series, see appendix A.

Book Three of Four: Effectively Managing LDS Data

LDSs provide more information, but these multitudes of new data must be accurate and 

trusted, collected once, protected vigilantly, and used frequently. This third book in 

the guide series discusses organizational issues aimed at moving the project forward and 

ensuring that the data are of high quality so that users may leverage them with confidence 

for data-driven decisionmaking.

	  The introduction explains the purpose of book three and describes the 

conventions used throughout the series.
 

 

 

 

 

 

 

 

 

 

 Chapter 1 introduces the concept of data governance, and discusses the benefits 

of this structure and process for data management. 

 Chapter 2 lays out a number of basic steps to establishing effective data 

governance. 

 Chapter 3 describes the key groups and individual roles that may comprise an 

education agency’s data governance structure. 

 Chapter 4 defines “quality” data. 

 Chapter 5 discusses the various organizational processes that facilitate the 

creation and maintenance of quality data. 

 Chapter 6 discusses the value of data standards, and describes the major sources 

of such standards. 

 Chapter 7 provides a basic overview of issues and relevant laws regarding the 

protection of student data. 

 Chapter 8 addresses the need to secure the LDS data to prevent unauthorized 

access and tampering. 

The appendices include an overview of the four books in this series, references, 

additional resources, a handout on data governance, data governance supplementary 

materials, and other relevant Forum and NCES resources. 

The National Forum on Education Statistics 

The work of the Forum is a key aspect of the National Cooperative Education Statistics 

System (the Cooperative System). The Cooperative System was established to produce 

and maintain, with the cooperation of the states, comparable and uniform educational 

information and data that are useful for policymaking at the federal, state, and local levels. 

To assist in meeting this goal, the National Center for Education Statistics (NCES), within 

the U.S. Department of Education, established the National Forum on Education Statistics 

(the Forum) to improve the collection, reporting, and use of elementary and secondary 

education statistics. The Forum deals with issues in education data policy, sponsors 

innovations in data collection and reporting, and provides technical assistance to improve 

state and local data systems. 

Development of Forum Products 

Members of the Forum establish task forces to develop best-practice guides in data-related 

areas of interest to federal, state, and local education agencies. They are assisted in this work by 

NCES, but the content comes from the collective experience of the state and school district 

task force members who review all products iteratively throughout the development process. 

Documents prepared, reviewed, and approved by task force members undergo a formal public 

review. This public review consists of focus groups with representatives of the product’s 

intended audience, review sessions at relevant regional or national conferences, or technical 

reviews by acknowledged experts in the field. In addition, all draft documents are posted on 

the Forum website prior to publication so that any interested individuals or organizations can 

provide feedback. After the task force oversees the integration of public review comments and 

reviews the document a final time, publications are subject to examination by members of the 

Forum standing committee sponsoring the project. Finally, the entire Forum (approximately 

120 members) reviews and formally votes to approve all documents prior to publication. 

NCES provides final review and approval prior to publication. 

vi Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Contents
 

The Longitudinal Data Systems Task Force ............................... iii
 

Acknowledgments .................................................. iv
 

Foreword .......................................................... v
 

Introduction......................................................... 1
 

Part i. governing the Data................... 7
 

Chapter 1. What Is Data Governance 


and Why Does It Matter?........................................... 9
 

Chapter 2. Basic Steps to Establishing Data Governance . . . . . . . 17
 

Chapter 3. Teaming Up: The Groups, Roles, and 


Responsibilities of Data Governance ............................. 27
 

Part ii. improving Data Quality ......... 43
 

Chapter 4. What Are Quality Data? .............................. 45
 

Chapter 5. Data Quality From Bottom to Top................... 47
 

Chapter 6. Speaking the Same Language: Data “Standards”.... 51
 

Part iii. Securing the Data, 


Protecting the individual .................. 61
 

Chapter 7. Privacy and Confidentiality .......................... 63
 

Chapter 8. Data Security .......................................... 69
 

Appendices ......................................................... 77
 

A. Overview of the LDS Guide Series ................................... 77
 

B. References ...................................................... 79
 

C. Additional Resources.............................................. 81
 

D. Handout: The Ungoverned Education Agency.......................... 97
 

E. Data Governance Supplementary Materials ............................ 99
 

F. Forum and Other NCES Resources ................................. 103
 

Book Three of Four: Effectively Managing LDS Data vii 

introduction 

T his book, Effectively Managing LDS Data, is the third in a four-part series 

about longitudinal data systems (LDS). The first book in the series, What 

is an LDS?, focused on the fundamental questions of what an LDS is (and what it is not), 

what steps should be taken to achieve a sound system, what components make up an 

ideal system, and why such a system is of value in education. The second book, Planning 

and Developing an LDS, focused on the early stages of LDS development to help state and 

local education agencies determine what they want their LDS to accomplish and what 

they need to achieve these goals. The present installment discusses organizational issues 

aimed at moving the project forward; and at ensuring the data are of high quality so that 

users may leverage them with confidence for data-driven decisionmaking. It looks at the 

establishment of governance structures and processes, getting the right people in place, 

and creating committees and working groups of diverse expertise to oversee and inform the 

process. This process is ultimately aimed at improving data quality and increasing the use 

of those data to improve education. This book also explores ways to ensure data quality 

through staff training, validation procedures, and establishment and adherence to data 

standards. Finally, the document discusses the challenges of securing the system to protect 

individual privacy and the confidentiality of education records. 

Figure 1 lays out the major issues discussed in each of the four books in this series. 

For more information on the purpose, format, and intended audience groups of this guide 

series, see Book One: What is an LDS? 

Book Three of Four: Effectively Managing LDS Data 1 
2 Traveling Through Time: The Forum Guide to Longitudinal Data Systems

Figure 1. Summary of the Forum Guide to LDS Series

Book I: What Is an LDS?

 Understanding what an LDS is (and is not)

 Appreciating the organizational steps needed to 

institute and effectively use an LDS

 Identifying the technical features and 

capabilities of an effective LDS and the 

additional features that can enhance the 

system’s utility

 Recognizing the benefits of an LDS

Book II: Planning and 

Developing an LDS

 Engaging stakeholders

 Describing the current system 

 Envisioning the desired system

 Defining needs, including data and functionality

 Gaining buy-in and funding

 Building relationships

 Writing an RFP 

 Building or buying a system or components

 Transferring knowledge (e.g., from developers 

to staff)

 Defining and measuring success

 Refining the system

Book III: Effectively 

Managing LDS Data

 Defining governance structure

 Defining roles and responsibilities

 Collaborating to improve data quality and 

streamline operations

 Managing changes to the system

 Training staff to ensure data quality

 Auditing/validating data at all levels 

 Establishing/following data standards

 Securing data to protect privacy 

 Providing users access to key data

Book IV: Advanced 

LDS Usage

 Collecting, storing, and delivering key data 

 Developing useful reports to fulfill common 

data requests and needs 

 Developing user-friendly data tools to facilitate 

access and analysis

 Training users to utilize the technology

 Building awareness, understanding, and 

analytical capacity
 LDS Lore: 

The ungoverned education agency 

The history and consequences

The state education agency’s data collection and management practices had come about over time, driven mainly by compliance and funding. Various program 

areas were created to focus on specific federal surveys, and staff collected the data 

needed to do their jobs. Program area staff administered the surveys, followed their 

own quality assurance processes, and maintained and secured the data in their 

own silo systems. And, of course, data were reported as required by the federal 

government. Individual managers took their own approach to directing staff and 

organizing work, and coordination across program areas was limited. Over time, different departments began to collect some of the same data elements. Inconsistencies were commonplace. For instance, while the student 

information system listed an “Aileen Hutchinson,” who was not in special 

education, the Special Education Department’s system included a girl named “Allie 

N Hutchinsen.” Despite these discrepancies, the staff knew the two records referred 

to the same student based on other directory information. However, structural 

differences between the systems—different definitions, formats, and option sets— 

further complicated matters. For example, Allie’s race, White, was coded as “1” in 

one database and “2” in the other because the systems used different option sets. 

Furthermore, because program areas defined their own data elements and used 

different software to manage them, the ability of the agency’s many data systems 

to “talk” to one another varied from limited to nonexistent, burdening staff with 

redundant data entry work and introducing errors into the system. With no clear 

requirement for documentation of data processes, methodologies often changed, 

as happened when Joe left the agency and no one else knew how to produce the 

dropout rate. The new guy, Steve, calculated it the way he had at his previous 

agency. No one saw a problem with this, especially since Steve’s numbers were lower 

than Joe’s. But when the time came to compare the new rates to previous years,’ 

some staff realized that they were comparing apples to oranges. Year in and year out, the work was done, but specific tasks weren’t assigned to anyone in a consistent manner, and sometimes reports were late or incomplete. 

And since there was no “official” source for each data element, data for federal 

reports might come from one source one year, another the following year. 

Book Three of Four: Effectively Managing LDS Data 3 
Similarly, without clear guidelines, staff fielded data requests as best they could. When inquiries came in, the recipient would decide to which program area 

the request should be sent. In one instance, Talia was asked for the school addresses 

in a district. While McKenna and Vita both managed school directory data in 

separate systems, Vita was given the task because her desk was near Talia’s. When postsecondary education leaders asked about sharing data, agency officials cried “FERPA!,” invoking the federal privacy law because they mistakenly 

thought it prohibited such exchange (see chapter 7). Security and access protocols 

were not well understood, and staff often took a lax approach to protecting sensitive 

information (see “Identity theft in the printer room” in chapter 8). Data quality 

problems were handled as they arose, but no long-term changes were made to 

ensure the same issues wouldn’t crop up again—like treating symptoms as they 

flare up, but never working to cure the underlying illness. And since no responsible 

group or process was in place to identify the sources of the agency’s problems, they 

went undiscovered and data quality issues were just blamed on IT. This went on 

for some time and, other than the techies, most staff didn’t see much of a problem. 

It was simply business as usual. 

The realization 

Over time, the country became more interested in education data. Education 

stakeholders wanted more information for accountability purposes and to better 

understand what programs and instructional strategies worked. They wanted data 

to inform decisionmaking at all levels; and to improve administration, instruction, 

and student performance. The bottom line was, they wanted data from across 

the agency and they wanted them fast. This changing environment posed many 

problems for the agency. Requested analyses required linking across silos, or data 

integration in a central data store. Before this could happen, duplicate, inconsistent 

data had to be reconciled. However, once the integration work began, more 

inconsistencies were discovered than anyone had imagined. Data quality had to be 

a higher priority; security had to be improved; and the data elements collected had 

to serve business and stakeholder needs, not just meet federal requirements. Better 

methods of sharing data had to be devised if the agency was to meet the growing 

demand for a “P–20” system. And better, more consistent protocols were needed to 

make data sharing more efficient and prevent improper dissemination. The chief 

information officer decided something had to be done. Having seen a presentation 

at a national conference, he was convinced a process called “data governance” 

could help address the agency’s problems. 

4 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Conventions 

Throughout this series, important terms and topics will be highlighted in sidebars. Notable 

subject matter will be easily identified by the following icons: 

Definitions LDS lore 

(fictional tales) 

Key points

District 

difference 

Bright ideas Tips 

Book Three of Four: Effectively Managing LDS Data 5 

governing the Data 

Part I 

W hile certain other sectors have long benefited from good data governance, 

the education community is relatively new to the concept. Every education 

agency has some means of handling its data but, historically, many have grown multiple, 

program area-specific approaches and cultures rather than implement a coherent, wellorchestrated, enterprise-wide system of data governance. Moreover, responsibility for 

data has too often been unclear or misplaced. For example, data ownership has often 

been placed with technology staff, who are already responsible for the infrastructure 

that collects, stores, and shares the data; rather than with program area specialists who 

have a deeper understanding of the data. This reality, despite its inefficiencies, may have 

worked well enough when data were only used for compliance purposes. However, the old 

processes for managing education data must change as the goal of education information 

systems continues to expand to providing broad access to, and facilitating effective use of, 

the data. By helping to create greater order, focus, and efficiency, the implementation of a 

strategic, enterprise-wide system of data governance can help agencies meet their modern 

goals of data-informed education. 

Establishing data governance is crucial to LDS success and should ideally be the 

first step in the system’s development—before implementation begins, before plans are 

even drawn up. A major effort like LDS implementation expands data quantity and 

access, and heightens the need for data quality and security. Therefore, a coordinated 

approach to identifying data issues, creating solutions, and communicating decisions 

is critical to meeting stakeholder needs. Developing an LDS may allow your agency to 

collect, maintain, and share data; but without the right policies, processes, and staff in 

place to ensure quality, these data may be problematic. Data governance is crucial to 

LDS success, as it creates a culture of accountability, collaboration, and standardization 

around information. No surprise, then, that LDS development often spurs interest in data 

governance and provides the opportunity for an agency to spend the time and resources 

necessary to implement a strong data governance process. 

Book Three of Four: Effectively Managing LDS Data 7 
 

District difference 

Data governance at the state and district levels will look very similar. 

In fact, the information offered in these chapters should be easily 

transferable to the local level with just one minor and obvious tweak: 

when involving data suppliers in the process, representatives will come 

from schools rather than districts. 

LDS Lore: 


Data governance, ASAP
 

The agency had planned its ideal system and hired a vendor to build it. Eventually however, staff began to 

question the quality of the agency’s new student-level data 

being collected and housed. The agency’s effort to integrate 

records from multiple sources was exposing countless 

inconsistencies and inaccuracies, and it became apparent 

that potential users would shun the data if they didn’t 

trust them, and rightfully so. Several data-sharing blunders 

had already occurred, potentially exposing sensitive 

personal student and teacher information. In addition, 

while demand for the new data was high and continued to 

increase, the staff were having trouble keeping up and data 

request processing was erratic and slow. The LDS investment 

would be wasted if these problems persisted, and something 

had to be done to improve data quality and secure the 

agency’s information assets. 

8 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Book Three of Four: Effectively Managing LDS Data 9

What is Data governance and 

Why Does it matter?

Chapter 1

is in charge: it is about identifying existing or potential data problems and fixing them 

to prevent them from happening or recurring. As a continuous and iterative process, data 

governance is a systematic way of handling data throughout the information life cycle, 

from definition to retirement (see chapter 1 of Book Two: Planning and Developing an LDS).

The process fosters coordinated responses to ongoing data quality issues and, eventually, 

a shift to proactive action to stem these problems before they occur. An environment 

is created in which technical and business data issues can be resolved and prevented 

in a collaborative, efficient, and transparent fashion. This coordination should extend 

beyond the compartmentalized program areas and across the business/technology divide, 

throughout the education agency and even to school districts and other organizations 

such as postsecondary institutions, the labor department, social services, and others with 

valuable data related to student histories and outcomes. 

D ata governance can be defined as both an organizational process and a 

structure; it establishes responsibility for data, organizing program area staff 

to collaboratively and continuously improve data quality through the systematic creation 

and enforcement of policies, roles, responsibilities, and procedures. As a structure, clear 

and specific roles and responsibilities are assigned and staff are held accountable for the 

quality of the data they manage. Ultimately though, data governance is not about who 

Data governance is both an organizational process and a structure; 

it establishes responsibility for data, organizing program area 

staff to collaboratively and continuously improve data quality 

through the systematic creation and enforcement of policies, roles, 

responsibilities, and procedures.
10 Traveling Through Time: The Forum Guide to Longitudinal Data Systems

Benefits of Data Governance

In an education agency, a data governance initiative typically aims to improve at least three 

major areas: organizational coordination, data quality, and data use. Figure 2 presents the 

benefits strong data governance can provide. Operational improvements at the bottom of 

the chart lead upward to improve the agency’s data quality, which then facilitates more 

effective and widespread use of data to improve education. 

Figure 2. Aims and benefits of data governance

Increased use of data to improve education 

Higher quality data

 More accurate and reliable data  Timelier access to data

 More useful data  More secure data

Greater organizational coordination and collaboration 

 Establishment of clear ownership,  Standardization of business processes 

responsibility, and accountability over time

 Reduction or elimination of redundant  Shift in mode of operation from reactive 

efforts (e.g., data collections) to proactive

 More frequent, broader, and better  Enhanced understanding of data assets

quality communication and collaboration

Greater organizational coordination and collaboration

A more holistic, cooperative approach to handling data can be established through data 

governance. The decompartmentalization and coordination of enterprise-wide efforts 

can improve the culture of data collection, maintenance, use, and dissemination. More 

specifically, data governance accomplishes several goals.

Data governance goes beyond LDS

While data governance is a key factor in LDS success, it provides even 

broader benefits in terms of how an agency manages data, ensures data 

quality, and fosters effective use of those data. In effect, any data initiative 

will benefit from good data governance.
Establishing clear ownership and responsibilities 

As the adage goes, when something is everyone’s responsibility, it is no one’s responsibility. 

Data governance assigns responsibility for each and every data element and deliverable 

to a single data steward, who becomes their “owner.” The roles of program area staff are 

specifically laid out to avoid confusion, and to ensure the necessary work is completed and 

only one person is accountable for a particular data problem. For example, if discipline 

data are requested, everyone should know which program area staff member is the 

discipline data steward and this person, therefore, should respond to the request regardless 

of who was initially called. In turn, if an issue arises with the discipline data in a legislative 

report, or on the annual report card, that same staff member is held accountable and is 

responsible for resolving it. In other words, data stewards are responsible for every aspect of 

the data they own, from collection to reporting to communication and so on. Thus, data 

governance helps agencies maintain an orderly operation in which every job is defined and 

every job is done. Again, responsibility for the data should be seated with program area 

staff, not the technology staff. The role of IT should be to support the agency’s business 

needs. Responsibility for the contents of those systems—the data—should rest firmly with 

program area staff. 

Reducing or eliminating redundant efforts 

With data governance, staff work to identify and eliminate collection redundancies 

wherever possible. As a result, data elements are shared by all appropriate program areas, 

but collected only once by a single area rather than multiple times within or across 

departments. Each element used for federal or state reporting or dissemination has a single 

authoritative source. Data elements are collected at the individual-record level, rather than 

in aggregate form, and all aggregate collections end. Ownership of, and responsibility for, 

all deliverables are clearly documented and communicated, avoiding duplication among 

multiple staff members. These activities may dramatically reduce the reporting burden 

of school districts that must report the data, as well as processing time for state staff. 

Ultimately, data governance will help the agency realize the “collect once, use many times” 

ideal, improving efficiency and effectiveness as well as the quality of the actual data. 

Facilitating more frequent, broader, and better quality 

communication and collaboration 

Data governance forges lines of communication among a variety of stakeholders. The 

process is a mechanism for consistent transmission of expectations across program areas 

within the department, as well as externally with districts and other agencies and partners. 

Standards for data reporting and collection, and changes to those standards, are clearly 

broadcast. These include definitions, formats, business rules, responsibilities, and due 

dates. Beyond facilitating more effective communication, data governance brings affected 

stakeholders together to collaboratively plan their work and address data-related issues. 

Program area staff, IT, leadership, districts, and other relevant parties make decisions 

collectively rather than, for instance, the state agency staff making the decision and simply 

telling the districts what was decided. Districts and all other affected stakeholders are 

invited to weigh in on data issues that affect them, and to help create smarter solutions. 

They are involved in determining new requirements and making sure these can be met, 

and they also help find ways to make the data more relevant at the local level—a key to 

ensuring data quality. In short, with data governance, all decisions are made collaboratively 

and communicated effectively. 

IT staff “own” 

the infrastructure. 

Program area staff 

“own” the data. 

Data governance 

helps agencies 

achieve the 

“collect once, 

use many times” 

ideal. 

Book Three of Four: Effectively Managing LDS Data 11 
Standardizing business processes over time 

Data governance brings staff together to define enterprise-wide standards for each data 

element, and to normalize the procedures for data reporting and collection. Clearly 

defined, documented, and well-communicated policies and processes let everyone know 

what needs to happen, by whom, how, and when. These processes are documented and 

strictly followed, and each one has a single “owner.” For example, while calculating the 

National Governors Association graduation rate* may involve multiple staff members, 

only one data steward should be responsible for ensuring that the rate is calculated on 

time, properly, and in a consistent manner. Guided by clear data management protocols 

(regarding collection, reporting, sharing, etc.) that are consistent within and across program 

areas, staff members no longer operate in their own fashion within their own “silos,” and 

the same tasks are no longer performed differently by different people. Everyone, including 

districts, knows what to expect and what is expected each cycle. Documentation of these 

processes also helps to ensure sustainability over time despite staff turnover, as well as to 

increase transparency by detailing processes and clarifying the origins of the data. 

LDS Lore: Data quality from

management simplicity 

More and more work was being done, but the quality of the state agency’s data wasn’t improving. In 

fact, the staff’s lack of coordination was just making 

things worse. Cedric and Mark worked together 

to compile the data for a federal report, running 

some quality assurance tests on the district data, 

aggregating them, and building tables. Meanwhile, 

Amy, the staffer who worked on the prior year’s report, 

had some free time on her hands. Poking around the 

agency system, she found the new file and took a look. 

Some of the numbers seemed off, so she ran some code. 

Her results seemed better than the ones in the file, so 

she pasted them in... 

* The National Governors Association (NGA) graduation rate is a standard, four-year adjusted cohort graduation rate 

agreed upon by all 50 governors in 2005. 

12 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Months later, districts started calling to say that some of the numbers in the file were off. Mark 

and Cedric reviewed and re-ran their code to identify 

the problem, but they couldn’t replicate the reported 

numbers. No one could figure out what had happened. 

Eventually, word got around and Amy came forward 

and admitted what she had done. 

The message was clear: Without the assignment of clear roles and responsibilities, and the adherence 

to clear processes, confusion will result, work will be 

duplicated, errors will be difficult to trace and resolve, 

and time will be wasted. In this example, less is more 

when it comes to data quality, and quantity of work 

does not necessarily translate into quality of data. 

Shifting operations from reactive to proactive mode 

Naturally, the data governance process will begin as a reactive course of action that deals 

with fixing problems that already exist. But, as the pressing problems are solved, the focus 

will eventually shift to identifying areas that can be improved, and to preventing mistakes 

from arising in the first place. For instance, if a state report card lists two discrepant counts 

pulled from two different silos, the agency’s reactive response should be to bring the two 

data stewards together to figure out if there is a reason to use both sources. If not, the next 

step is to determine which system is the authoritative source for the requested information. 

A proactive process, on the other hand, would have identified the redundancy before 

report card season, when everyone worked together to catalog all of the agency’s 

collections and data elements. 

Enhancing understanding of the organization’s data assets 

Through the governance process, staff become more aware of the data the agency 

collects, and learn which data stewards are in charge of which elements. At a higher level, 

data governance may help spur a culture shift from viewing data as compliance-driven 

to viewing them as assets that can help improve their work and student performance. 

With student-level longitudinal data, agencies can do more than fulfill compliance 

requirements—they can help improve programs and policies. The data governance process 

helps stakeholders shape the system to better meet their needs and to expand their 

capabilities. 

Book Three of Four: Effectively Managing LDS Data 13 
Higher quality data 

Implementing a longitudinal data system does not in itself ensure higher data quality. 

However, it provides an opportunity to improve data quality by bringing errors and 

inconsistencies to light through the enterprise-wide integration of disparate silos. Without 

a systematic approach to governing data, the organization has no means of addressing 

these issues. As a result, the masses of information collected and maintained in the LDS 

will be questionable and may not meet stakeholder needs. Consequently, many agencies 

see improving data quality as the primary reason to focus on governance. More specifically, 

effective data governance will help in several ways. 

Improving accuracy and reliability 

Data governance serves to increase alignment among program areas, ensuring consistency 

in data and related management procedures. Data are more reliable when data managers 

come together to lay out clear definitions and other standards, and data stewards work 

to identify and correct any deviations from those standards. In addition, thorough 

and consistent validation procedures starting at the local level ensure data accuracy; 

and authoritative data sources are identified and redundancies eliminated, creating a 

single “truth.” These processes ensure that the agency collects data elements only once, 

streamlining data reporting and making analyses more consistent. 

Increasing data usefulness 

Data will be more useful when they are aligned with the needs of program areas and other 

stakeholders, rather than driven by information technology. Once the data governance 

process helps staff identify, manage, and control all the data, and those data become 

trusted and reliable, then another level of data governance may manage the use of that 

information. 

Providing timelier access 

Data governance leads to timelier information by increasing the efficiency of data 

collecting and reporting. Furthermore, districts receive ample notice of changes and, thus, 

have more time to prepare and work out potential issues. Standard business processes at 

the state level may also save significant time. Streamlined data-sharing procedures, for 

instance, can improve how quickly requests are processed, giving users faster access to 

the information they need. Data governance also serves to standardize records processing 

from year to year, helping to eliminate time wasted figuring out who will do what and 

preventing the need to reinvent the wheel every collection cycle. 

Improving security 

Within a strong data governance system, staff from program areas and IT work together to 

determine the sensitivity of each data item and implement effective protections. Clear and 

consistent data-sharing processes streamline and coordinate agency efforts and help prevent 

improper release of sensitive data (see chapters 7 and 8). 

Increased use of data to improve education 

While many see high-quality data as the primary goal of data governance, the ultimate 

benefit may be their increased use by legislators, administrators, and educators to improve 

education. For data to be used, they must be accurate and trusted, timely, and designed to 

meet stakeholder needs. Data governance helps agencies realize these goals by coordinating 

Data governance is 

about people and 

policies. Technology 

supports the 

process but should 

never drive it. 

14 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
staff from across the enterprise to collectively solve data issues. Clear data ownership 

ensures that the right staff members are disseminating and answering questions, and 

better communication increases all users’ understanding of the information collected 

by the agency. Better quality data equip decisionmakers to improve resource allocation 

choices, and better student data enable educators to enhance their students’ instructional 

needs. In addition, data governance can culminate in smoother operations and better 

decisionmaking based on the foundation of timely, secure, high-quality data aligned with 

agency goals. 

These benefits should far outweigh the costs of implementing data governance. If, 

in fact, your agency has the in-house capacity to implement a data governance process, 

the cost of a data governance program can be as low as zero. While some state or local 

agencies will decide to hire an outside consultant to drive the process, others will need to 

invest only their time. Though changing the way the organization operates and improving 

data quality will take time, persistence will eventually lead to greater efficiency; time and 

resource savings; better programs and policies; higher quality information; and, ultimately, 

better student outcomes. 

Book Three of Four: Effectively Managing LDS Data 15 

Basic Steps to establishing 

Data governance 

Chapter 2 

I deally, a good data governance system will be in place before LDS 

development begins. However, if your organization has broken LDS 

ground without establishing an effective governance structure, it is never too late to start. 

And, even if your agency has implemented a governance structure, data governance is an 

iterative process that can always be improved. 

Gauge your governance 

How governed is your organization? The Data Administration Newsletter offers 

a data governance test to help you evaluate your agency in eight areas of data 

governance. The test is available at http://www.tdan.com/view-articles/10149 

and is geared towards building the case for data governance to leaders of your 

organization. 

Although no single approach is best for implementing a data governance initiative, 

some key action steps should always be taken on the road to good data governance. 

Normally, chief information officers (CIO) initiate the data governance plan. However 

they, or other leaders, come to realize their organization would benefit from better data 

governance, they should begin with a careful and honest appraisal of the agency’s approach 

to data management, weighing the enterprise’s current levels of coordination. For tools to 

help in this period of reflection, see “Gauge your governance” above. 

After this stage of self-assessment come some key steps. Firstly, high-level executive 

support should be sought, followed by the assignment of key responsibilities to staff 

members and the formation of several groups central to managing and implementing the 

initiative. Data governance structures range from basic to elaborate in terms of personnel 

who serve the various functions (see chapter 3). However, the key action steps in a 

minimal, core data governance structure are: 

Book Three of Four: Effectively Managing LDS Data 17 
1. Seek executive support for the initiative. 

2. Create a Data Policy Committee. 

3. Assign a data governance coordinator. 

4. Identify data stewards. 

5. Create a Data Governance Committee. 

6. Identify, prioritize, and resolve critical data issues. 

7. Form working groups of data stewards as needed. 

In this chapter, the terms “data steward” and “data 

owner” are used to describe staff roles within a data 

governance structure, and do not imply technical 

or legal ownership. However, these terms are sometimes used elsewhere with 

meanings different from those applied here. For instance, by “owner,” real 

ownership of the data is not implied here in the way the Family Educational 

Rights and Privacy Act (FERPA) outlines. That is, at an organizational level, 

a state education agency (SEA) may be considered a data steward when 

managing data on behalf of a district, in this case the data owner. Or, similarly, 

a district might be referred to as a “data steward” for managing data from its 

schools, which are the “data owners” at this more granular level. 

1. Seek executive support for the initiative 

Appeal to agency leadership to gain buy-in for the data governance initiative. The specific 

leaders who should be on board will vary depending on the state’s size and staff. In small 

states, support might come from the education chief; while in bigger states the appropriate 

executive might be on the chief’s staff, perhaps an assistant commissioner or deputy. 

Communicate the costs of the status quo and the benefits that will result from greater 

coordination. Executives must understand the value of the agency’s data and the need 

to ensure effective management of those information assets, and to improve quality and 

security. To gain support, present qualitative and, ideally, quantitative costs of poor data, 

redundant efforts, and insufficient security. Stress the tangible benefits of a more strategic, 

enterprise-wide approach to data management that coordinates policies, processes, and 

architecture to improve data quality; aligns work across the agency and streamlines 

operations; more effectively protects the data; and shares information in a more systematic 

and timely manner (NASCIO 2008). In making your case for data governance, try the 

following compelling arguments. 

Fewer errors resulting in lost funding. 

Stress real or potential examples of loss of funding due to late or inaccurate reporting 

to the federal government. Explain how data governance can streamline processes and 

increase data quality; as well as ensure that the state and districts get the funding they 

require to meet students’ needs. For example, if the special education data were late last 

year, explain how this might have been avoided with clearer standards and requirements, 

better communication and collaboration with the districts, improved validation 

procedures, and the sharing of best practices among data stewards. 

18 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
More efficient use of resources. 

Explain that increased data accuracy and transparency possible with individual-level 

collections and data governance enable more appropriate allocation of resources. With an 

individual-level data collection, one can see where the numbers come from rather than 

just rely on a district tally. For instance, while last year’s submission might have included 

50 English language learners, this year’s student collection may only include 15 individual 

students. While the aggregate number may be difficult or impossible to verify, the 

individual records show exactly where the numbers come from. 

Saved resources and time through fewer resubmissions, 

corrections, and audits. 

By improving communication between the state and districts, and facilitating collaboration 

around data issues, data governance reduces the time and money spent fixing bad data 

(through multiple resubmissions by districts, for example) or auditing districts with 

problematic data. Better quality data from the start will save time and resources later on. 

Economic benefits for the state. 

Beyond the effects on the education system, education data may affect sectors of the state’s 

economy. For instance, a mistakenly high number of dropouts may decrease a school’s 

ratings and negatively impact real estate values in the area. 

Increased ability to identify common data quality problems 

among districts, and to target interventions. 

Through better coordination with districts, states are able to more easily identify districts 

experiencing similar difficulties. For example, if different districts use different vendors, the 

state might determine that a particular vendor needs to make improvements in a certain 

area. Or, districts might be targeted for professional development or the introduction of 

new validation procedures. 

Fewer headaches in general. 

Finally, invoke unpleasant memories, such as phone calls from irate constituents and 

school staff about bad data. Then explain how these problems could have been avoided 

had a data governance process been in place. 

The importance of executive support for a data governance initiative cannot be 

overemphasized. Strong and continued commitment from organization leaders will not 

only provide needed resources but, more importantly, will support the culture change 

needed in a data governance effort, applying pressure from the top and providing the 

authority needed to enforce contentious decisions. For instance, a data governance group 

may make a decision that prompts backlash by a key staff member (perhaps responsibility 

for a data element is shifted from one staff member to another; or a program area’s data 

are included in the LDS without the department’s full support). Without pressure from 

leadership, staff resistance could undermine the process. Sometimes, just getting staff 

to show up for data governance meetings can be challenging if leaders do not stress the 

importance of participation or even make attendance mandatory. If possible, ask leaders to 

require data governance group members to send qualified substitutes in their stead when 

they are legitimately unavailable. 

The good news is, your agency’s leadership is unlikely to willfully resist a data 

governance plan. Commonly, executives just need to be shown how data governance will 

benefit the organization. At the very least, staff need to inform leaders of the initiative; 

ideally, executives will support and participate in the entire process. If leadership support 

Book Three of Four: Effectively Managing LDS Data 19 
LDS Lore:
 

The meeting mandate
 

Yori, the data governance coordinator, paced back and forth across the floor of the nearly empty board room. He didn’t know why people weren’t coming to the 

agency’s second Data Governance Committee meeting. The first one seemed a great 

success at the time, but here he was with only two thirds of the group, waiting for 

other members to arrive. He knew that the data governance plan would derail if 

participation was low. Nevertheless, Yori put on his game face, took roll, and got 

down to business with those in attendance. After the meeting, he strode down to 

the education chief’s office. After a compelling appeal, Yori closed the chief’s door with a sense of accomplishment. He knew he’d sold her on data governance. The case was not even 

hard to make. Data governance, from his perspective, was a no-brainer: It was a 

common sense business solution for improving the department’s data. He had laid 

the case out clearly and simply, contrasting the department’s data management 

status quo with the potential benefits of the data governance initiative. Making 

the case even easier was the fact that, other than maybe some coffee and donuts for 

meetings, the costs to the state would be nearly zero. That afternoon, the chief sent out a brief email:
 “Participation in all data governance meetings is required. Staff 


assigned to these groups should attend all scheduled meetings, 


making them a priority over all other activities. If you are 


unavailable, please send a qualified substitute.” 
 Sure enough, the next meeting of the Data Governance Committee packed the board room. Yori smiled as he called the meeting to order, confident that the 

plan was back on track. 

cannot be quickly won, however, agencies should start or continue their data governance 

initiatives anyway, while continuing to seek high-level buy-in. Inevitably, this support will 

prove crucial. 

2. Create a Data Policy Committee 

The CIO should convene a group of executive management staff that includes the 

education chief (or other high-level agency staff member), the data governance 

coordinator, and executive leaders from each program area with a data steward. Rather than 

creating a new group, identifying an existing group that includes these members and asking 

20 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
them to occasionally focus on data governance may be preferable. This group’s main roles 

will be to establish the data governance policy; and to address issues that require executive 

support, such as those that affect multiple program areas and/or affect major agency 

reports or deliverables. (For a more detailed description of this group, see chapter 3.) 

3. Assign a data governance coordinator 

Assigning responsibility for overseeing the data governance initiative to one individual 

is absolutely critical. This staff member, the data governance coordinator, should be the 

“catalyst” for coordinating the data governance initiative, setting a cohesive action plan, 

and tirelessly pushing the process forward. Significant culture change must occur for data 

governance to take hold and make a difference in an agency. In fact, many initiatives fail 

because no single individual is in charge of making sure that the roles, responsibilities, 

and processes of the initiative are being followed on a daily basis. In the search for a 

coordinator, leaders may look for the following:

	 Tenacity. This individual must give the data governance process momentum,

setting goals and constantly following up on progress to keep the work moving 

forward.

	 Strong analytical skills. The coordinator must identify areas of needs and the

players needed to address them.

	 An ability to see the forest and the trees. This staff member should be able to see 

the overarching goals of the organization, as well as the details required to meet 

these goals.

	 An understanding of technology. The coordinator should understand technology

and be able to bridge the divide between program area staff and IT.

	 An education background. This individual must understand why the

organization is actually collecting the data and why they matter (it’s about the 

students, not the data).

	 Good mediation and communication skills. This staff member must be able to

bring people together to work through difficult and sometimes contentious issues. 

(For a more detailed description of this role, see chapter 3.) 

4. Identify data stewards 

Identify areas of data and assign a data steward to each one. Articulate specific responsibilities, 

making each and every data element the responsibility of a single steward. For example, 

each data steward should be responsible for data elements, rather than for databases. 

Data stewards should “own” specific contents of the system regardless of where the data 

physically reside (e.g., on a desktop, in a database, or in a central LDS). A clear process 

should be created for identifying data stewards. For instance, leaders may look for staff who 

	 have business subject matter expertise and work directly with data (not 

supervisors); 

	 are knowledgeable about data and their educational context, i.e., the programs 

and policies (preferably not techies); 

	 serve as points of contact for districts’ questions about a program area’s data; 

	 are frequent users of data and are comfortable with databases and querying; 

	 prepare data for federal and/or state collections; 

Book Three of Four: Effectively Managing LDS Data 21 
22 Traveling Through Time: The Forum Guide to Longitudinal Data Systems

	 are detail-oriented and understand how to review data for accuracy; and

	 appreciate the value of quality data.

Of course, many education agencies will not have personnel with all of these abilities 

in every program area. But, as a general practice, rather than hiring new staff to serve as 

data stewards, start with existing personnel and provide support to grow necessary skill sets 

and knowledge. Try to identify the best “fit” possible to lessen training requirements. The 

data governance coordinator should be responsible for identifying gaps in knowledge and 

skills, and for providing necessary professional development and coaching. (For a more 

detailed description of this role, see chapter 3.)

5. Create a Data Governance Committee

Form a cross-area group of data management staff that includes the data governance 

coordinator, data stewards, and other key staff members. This group will be the core of the 

data governance process, and will effect most of the collaboration and decisionmaking. 

The data governance coordinator should chair the group and oversee the agenda. Early in 

its formation, the group should collectively agree upon a mission statement, which should 

include the identification, documentation, prioritization, and resolution of critical data 

issues; and to core goals and objectives (see appendix D). The committee should then meet 

frequently, perhaps monthly, to fulfill its stated mission. The coordinator and the data 

stewards should contribute agenda items for these meetings. Agenda items may include

	 federal reporting updates (e.g., from the EDFacts coordinator); 

	 technology updates, including any IT problems affecting data transmission or 

reporting; 

	 LDS project updates; and 

	 time for any member to raise issues not on the agenda. 

(For a more detailed description of this role, see chapter 3.)

6. Identify, prioritize, and resolve critical data issues

Document everything!

Every data governance detail should be documented. For example, who’s 

responsible for what? When are deliverables due? What are the critical data 

issues and what’s the status of their resolutions? What are the standard 

procedures? Documentation helps keep the work on track, prevents confusion, 

and allows staff to replicate activities from year to year and in spite of turnover. 

At each Data Governance Committee meeting, members should work to identify, 

prioritize, and resolve critical data issues, maintaining a log to track progress. Critical data 

issues are the organization’s data problems that must be addressed if the committee is to 
Master data management 

Master data management (MDM) refers to the ongoing process of identifying 

the authoritative source of data and ensuring that this source is consistently 

used to feed other data systems, or to populate the agency’s central data 

store; as well as for reporting, dissemination, and analysis. In this way, it is 

the answer to the “collect once, use many times” challenge. When key data 

elements are collected and used by multiple data systems, MDM is the process 

that determines which single source is authoritative. When integrating data 

from multiple sources into a central data warehouse, for example, authoritative 

(i.e., “master”) sources are identified for each element. And when new 

elements are collected, an authoritative source is assigned for each. When populating the 

data store with historical data collected before the MDM process began, the authoritative 

sources need to be determined for those older data items as well. MDM also keeps track of the 

data collected and maintained throughout the agency to ensure that common standards (data 

element names, definitions, codes, formats, etc.) are being used. When all of the agency’s 

past and present data are addressed, MDM’s will then focus on handling new data elements. 

MDM relies on both data governance processes and technological solutions. The data 

governance side of the process can be fulfilled through the Data Governance Committee, 

as it facilitates the collaborative designation of authoritative data sources and elimination 

of redundant collections. Technology solutions can then be used to share data among 

multiple data systems (“horizontal integration”) by updating secondary data in one system 

with the authoritative data from another. For instance, if the agency uses several operational 

databases, the student information system (SIS) may hold the authoritative student 

addresses, while the transportation system holds secondary student address data. The MDM 

application would feed the SIS data into the central data store and update the transportation 

system automatically whenever the information was changed in the SIS. 

Additional resources: 

 The Data Warehousing Institute 

http://tdwi.org 

This organization has produced a number of resources about master data management, 

including a tool to help agencies determine how much a data management solution would 

benefit them, as well as to assess how ready they are to implement such a solution. 

 Master Data Management 

http://nces.ed.gov/programs/slds/zip/master_data_management.zip 

This presentation on MDM was given by several state education agency representatives at 

the July 2008 Statewide Longitudinal Data Systems Grantee Meeting. 

achieve its core goals. One data steward should be responsible for each critical data issue 

and, at each monthly meeting, should update the group on progress made towards its 

resolution. At first, the data governance coordinator will likely identify many of the critical 

data issues. But as the process matures, data stewards should identify most of the issues. 

Examples of data issues that might be deemed “critical” are: 

	 a data collection that creates significant burden for school districts due to timing, 

collection mechanism, or duplication with other collections; 

Book Three of Four: Effectively Managing LDS Data 23 
 

 

 

 

 

	  reporting linked to funding that has been late, incomplete, or inaccurate; and
 

	  high profile reporting that has been late, incomplete, or inaccurate.
 

Prioritize these issues based on factors such as
 

	  time sensitivity;
 

	  the number of program areas affected; and
 

	  the data’s importance or how often they are used for federal reporting.
 

(Chatis Consulting)
 

On occasion, data issues will arise that require leadership-level support beyond the 

Data Management Committee. The data governance coordinator should bring such cases 

to the Data Policy Committee. 

LDS Lore: 

Integration-inspired indigestion
 

Adam dug his fingertips into the armrest. He, his supervisor, and other program area staff were in a meeting with 

the data governance coordinator and the CIO to discuss the 

agency’s plan to phase its data into the LDS. Yori, the coordinator, 

was going over the LDS project’s goals and timeline, and had 

just told Adam and his colleagues that their program area’s 

data would be part of the first phase of data migration into the 

LDS. Adam did not like this idea one bit. “That’s my data,” he 

thought. He felt he was losing control of a data set he’d managed 

for years and he didn’t want them dumped into some communal 

data store. As the meeting continued, he worried the data would 

be at risk in the LDS and he wasn’t swayed by the argument that 

the move was necessary to give users greater access to the records. 

“They already get enough access to the data,” he thought. Would 

it be harder for him and his staff to work with the data? He was 

accustomed to a good deal of autonomy, and he didn’t want to 

have to coordinate with other areas of the department. Suddenly, 

Adam’s stomach tensed. Was this the first step in phasing out, 

or scaling back, the staff? Would his job be at risk when the 

integration was complete? Then, twisting his feet around the 

chair’s legs, Adam realized that integrating “his” data into the 

24 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
LDS would probably expose the records to scrutiny. Errors might 

be discovered. He and his staff would be held accountable for 

poor quality data. This was bad... Across the table, Yori noticed the uncomfortable looks on some of the faces around the table. Adam, slightly 

contorted in his seat, seemed particularly uneasy. Seeking to 

reduce the growing tension in the room, Yori reviewed the plan 

again. He said that, while the integration had support from 

agency leadership and was nonnegotiable, there would be many 

benefits for the agency’s stakeholders and for the team itself. 

For example, integration would make it easier to create reports 

that were previously painstaking or impossible to generate, 

and conduct analyses across multiple program areas. For 

instance, they would soon be able to see how their attendance 

data correlated with discipline incidents and dropouts. Yori 

said the agency needed the team’s help to increase the system’s 

benefits, and asked the staff to consider what reports could be 

made available through the LDS to ease their workload and 

help districts. He also assured the team that while the physical 

location of the data would change, staff ownership of the data 

would not. 

After the meeting, Yori visited each of the data stewards and let them know they should feel free to ask questions or raise 

concerns whenever they have them. Knowing that some of the 

staff lacked data management experience, Yori made it clear 

that this effort was to ensure quality data, not punish people. The 

agency would offer a host of professional development sessions to 

inform staff and improve necessary skill sets. Yori also scheduled 

regular one-on-one meetings with data stewards to see how 

things were going and to answer questions. During his meeting 

with Adam, he explained that, while the data governance 

process was a major departure from how the agency had managed 

its data in the past, its objectives were aligned with high-level 

goals and the initiative had strong executive support. Change 

is difficult, Yori said, but he made it clear they were all in this 

together and the benefits would be worth the trouble. 

Book Three of Four: Effectively Managing LDS Data 25 
7.Form data steward working groups as needed 

As discussed earlier, a key principle of data governance is collaboration across the program 

areas of the organization. Many data issues affect multiple program areas but, without data 

governance, these issues are unlikely to be addressed in a comprehensive way. According 

to Chatis Consulting, when an issue arises, the Data Governance Committee should form 

a working group of stewards to collaboratively address the problems and craft a solution. 

Within the group, one data steward should be ultimately responsible for ensuring the 

working group resolves the issue. Groups should identify the problem and pinpoint its 

original source; define the goals of solving the issue; set up a clear and detailed strategy for 

a resolution; report back to the Data Governance Committee on progress; work with IT to 

implement the business solution; and, finally, communicate the resolution to all relevant 

stakeholders. (See appendix D for guidelines on how group meetings can be structured.) 

Track data problems to their source 

The identification of a data problem’s source is essential if the issue is to be 

resolved once and for all. If staff only treat the symptoms, the problem will 

likely surface again in the future. 

26 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Teaming up: The groups, roles, and 

responsibilities of Data governance 

Chapter 3 

I n an education agency’s data governance system, a range of responsibilities 

may be assigned to staff to manage the collection, maintenance, reporting, 

and use of data. This chapter provides a more granular view of the practical workings 

of the process, detailing the personnel and activities involved; and describing a sample 

set of groups, roles, and responsibilities your organization may choose to assign in its 

data governance structure. The information is presented generically and should not be 

considered the only possible data governance structure. Rather, it is a suggested framework 

that may be adapted based on your agency’s specific needs, staffing capacities, and 

available resources. 

Figure 3 on the following page presents a list of individual roles and groups that 

may constitute a data governance structure. As depicted in the figure, the data governance 

coordinator and data stewards work as individuals at the “core” of the data governance 

process. Several important peripheral roles may initiate, support, inform, or draw from the 

process. 

Several groups should also work to drive the initiative forward, identifying data 

issues and collectively creating responses. As discussed in chapter 2, the core groups 

include the Data Governance Committee and the Data Steward Working Groups. Two 

peripheral groups, the Data Policy Committee and the Data Request Review Board, also 

serve important functions. The discussion below consolidates many activities into these 

four groups, though your agency may assign some of these tasks to additional groups. 

Individual Role Descriptions 

The following draws on the Tennessee Department of Education (draft 2008), DQC 2008, 

EIMAC 2008, and Rozelle 2006 and 2008. 

Several players may be involved in a data governance structure, but a few roles are 

essential and should always be included. Specifically, every data governance structure 

should have a data governance coordinator, a group of data stewards, and a Data 

Management Committee. However, aside from these key functions, many of the roles 

and responsibilities may be assigned differently than in the manner outlined below. 

Book Three of Four: Effectively Managing LDS Data 27 
Figure 3. Key groups and roles of data governance 

Individual roles 

Education chief (or staff) 

Chief information officer (CIO) 

Data governance coordinator 

Data stewards 

Public information officer (PIO) 

Local education agency representative(s) 

Federal reporting coordinator 

Business analyst 

Core 

Groups 

Data Policy Committee 

Data Governance Committee 

Data Steward Working Groups 

Data Request Review Board 

What is important is not necessarily the roles you define, the titles you choose, or 

how the responsibilities are assigned to each individual. What matters is that all major 

responsibilities are clearly assigned and staff know their duties. In designating and 

managing these roles, at least three guidelines should be kept in mind: 

	 Develop and follow clear criteria for selecting staff members for each role. Careful 

assignment at the onset will help mitigate staff turnover later on. 

	 Provide ample support to help staff successfully meet their responsibilities. 

Getting everyone up to speed may take time, so persistence and patience are very 

important. 

	 Reexamine periodically why each role was designated and if each position is filled 

correctly. Make staffing adjustments if necessary. 

Education chief (or other high-ranking staff member) 

A high ranking executive such as the education chief, or other high-level executive should 

provide support for the data governance initiative. Some of this staff member’s specific 

data governance-related responsibilities may include 

	 participating in Data Policy Committee meetings; 

	 mandating staff participation in data governance groups; and 

	 exercising authority to enforce contentious decisions. 

28 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Chief information officer 

The chief information officer (CIO) usually initiates the data governance process within 

the organization; and should take the important early steps to establish the initiative and 

the team. Thereafter, the CIO should remain heavily involved in many of the structure’s 

ongoing activities. Some specific data governance responsibilities may include 

	 seeking executive support for the data governance initiative; 

	 forming, and serving on, the Data Policy Committee and the Data Governance 

Committee; 

	 working with the Data Policy Committee to develop the data governance policy; 

	 appointing the data governance coordinator; 

	 working with the data governance coordinator to identify the data stewards; and 

	 working with the Data Governance and the Data Policy Committees to identify 

and resolve critical data issues that require leadership support. 

Data governance coordinator 

One individual should lead the data governance process: the data governance coordinator, 

perhaps the most important player in the data governance structure. In fact, no agency 

should undertake data governance without this single overseer. The coordinator should 

drive the data governance agenda, direct data steward activities around data quality efforts, 

and ensure that data issues are resolved. Many data governance efforts fail due to a loss 

of momentum, and the data governance coordinator must work as a catalyst to keep the 

initiative moving (NASCIO 2008). This individual must focus on the mission of the group, 

keeping an eye on the organization’s overarching goals, following timelines and managing 

deliverables, reinforcing the principles of good data stewardship, and working with staff to 

determine how the agency can better handle its data. The coordinator must stay positive 

throughout the process because data governance involves significant culture change and 

this can be hard on staff morale: the constant scrutiny and the focus on improving agency 

practices can be disheartening. The coordinator must assure everyone that this is necessary 

and will benefit the agency in the long run. Some specific data governance responsibilities 

may include 

	 working with the CIO to identify the data stewards, maintain the data steward 

roster, and train and oversee data stewards’ work; 

	 working with the CIO to form the Data Governance Committee; 

	 leading the Data Governance Committee, including scheduling and facilitating 

meetings, preparing agendas, tracking and following up on action items, and 

recording and distributing minutes; 

	 participating in the Data Policy Committee meetings by taking data issues to 

leadership; 

	 convening, and participating in, the Data Steward Working Groups to resolve 

shared data issues (though individual data stewards should take on greater 

leadership responsibilities as the data governance process matures). 

	 working with the Data Governance Committee to identify, track, and resolve 

critical data issues—maintain a log of these issues and ensure a data steward is 


accountable for resolving each one (see appendix D for a sample critical data 


issues log);
 

Book Three of Four: Effectively Managing LDS Data 29 
Traveling Through Time:The Forum Guide to Longitudinal Data Systems 

LDS Lore:
 

Guns and governance
 

In its Sunday issue, the county newspaper reported that a local school had recorded ten firearm incidents during the previous school year. The following morning, the school was a ghost town. Frantic parents flooded the office with calls, demanding to know why they had not been notified of these 

incidents. The staff assured callers that the article was wrong and children could 

safely return to school: there had been no firearm incidents in the school’s history. 

Zero. A call to the newspaper revealed that the reporter found the information 

on the state’s website. Sure enough, after a few mouse clicks, the school principal 

located the error. But how could this have happened? 

At the state level, the problem was traced back to three possible staff members, but all three said that reviewing those particular data was someone 

else’s job this year. A call to the district found similar confusion: no one could figure 

out who had dropped the ball there either. Further sleuthing found that a simple 

typo by an overworked school office staffer had made its way through the quality 

assurance efforts of both the district and the state, and into the state’s LDS. One 

thing was sure: a pervasive lack of clear roles, responsibilities, and procedures had 

clearly caused the education community a lot of embarrassment, not to mention 

unnecessary anxiety at the school. 

 providing support to data stewards and other staff to ensure all involved know 

their responsibilities and are capable of meeting them; 

	 facilitating internal communication and collaboration about data quality issues 

between program areas, and between program areas and technology; 

	 facilitating communication with districts by serving as the main point of 

contact for data topics; and serving as the district liaison if a state program area 

undermines or deviates from the data governance policy (e.g., a program area 

makes a duplicate data request or provides inadequate notice before making 

changes to a collection); 

	 working with the Data Governance Committee and the IT division to develop 

and review standards for data elements (see chapter 6); 

	 maintaining a data collection and reporting calendar; 

	 creating and managing the data request approval process to ensure the accuracy 

and security of shared data (alternatively, the Data Request and Review Board 

may be responsible for this activity); and 

	 participating in national data conferences and member associations to stay abreast 

of best practices. 

30 
Book Three of Four: Effectively Managing LDS Data 

The “data owner” 

Some agencies distinguish “data owners” from “data stewards.” The “owners” 

may be program area directors assigned particular, higher level authority for 

specific sets of data, while much of the work related to managing those data is 

assigned to the “steward.” 

Whereas in the simpler data governance structure proposed in this chapter, 

these responsibilities are assigned to the data stewards, many agencies find 

this additional level to the hierarchy helpful, even critical. If your agency adopts 

this extra level of authority, each data owner must support the data governance 

process and understand that two-way communication with the data stewards is 

vital. A good rule of thumb is to keep the data governance structure as simple 

as possible, but whether or not that means adding another level of authority 

depends on the agency. 

Specifically, the data owners’ responsibilities may include 

 participating in the Data Governance Committee; 

 working with the IT division (specifically the security team) to determine 

the level of security required for each data element they “own”; and 

 approving all requests for the data for which they are responsible. 

Data stewards (a.k.a. data managers) 

Data stewards should be selected to manage the organization’s data, with a single steward 

charged with managing the data and held accountable for ensuring the quality of specific 

data elements within a particular program area. It is critical that every data element be 

the responsibility of a single data steward. Note that some agencies divide the steward’s 

role into two levels, including the additional role of data owner in their data governance 

structure as discussed in the box above (KSDE 2008). Some specific data governance 

responsibilities may include 

	 participating in the Data Governance Committee (if there is one, this may be the 

responsibility of the data owner); 

	 identifying and resolving critical data issues involving the data in their charge; 

	 participating in Data Steward Working Groups to collectively resolve data issues; 

	 working with the data governance coordinator to develop and review standards 

for data elements (see chapter 6); 

	 evaluating data quality (integrity, timeliness, accuracy, and completeness) and 

management (storage, reporting, adherence to policies, and data architecture); 

	 implementing data quality standards and all decisions of the Data Governance 

Committee; 

	 ensuring that the data dictionary and the data collection and reporting calendar 

contain the most current information about their data elements; 

31 
Traveling Through Time:The Forum Guide to Longitudinal Data Systems 

	 documenting and updating metadata related to data elements for which they are 

responsible; 

	 working with the IT division (specifically security) to determine the sensitivity of 

each element and the corresponding level of security and access rights required 

(while the security team may lead this process, stewards should have substantial 

input; if data owners are a part of the structure, they may fulfill this task instead 

of the data stewards); 

	 assisting users with the use and analysis of data; 

	 communicating to districts any changes in the way data will be collected, 

calculated, or reported; 

	 approving the release of data (for internal or external use); 

	 identifying opportunities to share and re-use data (e.g., for federal or state 

reporting); and 

	 staying abreast of laws that impact their data. 

Public information officer 

The public information officer (PIO) identifies and communicates data of interest to the 

public, and responds to data requests from the press. The PIO should be a member of the 

Data Governance Committee to stay apprised of data issues, and knowledgeable about 

ownership and data-sharing procedures. However, PIOs are not data stewards because they 

are not responsible for any data. When responding to press requests, agency procedures 

must be followed and data should be released only by the appropriate data steward to 

reduce the risk of inconsistent or inaccurate information. The PIO may also contribute 

to the Data Governance Committee by sharing news of any emerging public interest in 

certain types of data. (This role may also be fulfilled by the communications officer or 

other staff member with public relations responsibilities.) 

Local education agency representative 

As they are the source of all data the state agency collects, schools and districts should 

actively participate in the data governance process to ensure that local perspective informs 

all decisions. For instance, district representatives will know whether a proposed change 

to a collection can reasonably be met, and may suggest alterations to facilitate district 

compliance. Local education agency (LEA) staff can also offer information about district 

contracts, software, workload, and costs. They can help the state create solutions that will 

improve its communications and relationships with districts, and effectively decrease the 

local reporting burden. In addition, participating in the state’s data governance process will 

help increase the districts’ awareness of the importance of data quality and provide a model 

that could be implemented at the local level. At least one LEA representative should sit 

on the Data Governance Committee, but several would be preferable for a more rounded 

local perspective (representing districts of various sizes, from different geographic regions, 

etc.). Some states create an external group of districts and others, such as noneducation 

state agencies, specifically to review proposed changes to data collections. Some specific 

data governance responsibilities may include 

	 participating in the Data Governance Committee (as a member, not as a data 

manager); and 

32 
	 participating in Data Steward Working Groups to resolve data issues that directly 

involve the relationship and communication between the state agency and 

districts. 

LEA representative selection 

State education agencies (SEA) may find it useful to recruit local education 

agency (LEA) representation in the governance structure to increase 

perspective. To identify possible candidates, ask state program area leaders to 

identify district staff who 

 contact them frequently about data (both with questions and with 

suggestions for improving collections—LEA representation will ideally 

come from both ends of the spectrum); 

 care about data accuracy even when the data do not flatter the district; and 

 have a program area, rather than a technology, background. 

LDS Lore:
 

The effects of disorder trickle down
 

The districts were accustomed to supplying the state with a lot of data. Sure, they had to provide the same information multiple times, with some of the 

elements reported in both student-level and aggregate forms. Sure, changes to data 

collection requirements were often decreed without prior notice, and confusion 

about what was required and when it was due. And no, they didn’t really get 

much out of the deal—sending data to the state, but never getting anything useful 

back. If they realized the data were wrong due to a local or a state mistake, local 

staff often didn’t know whom to contact; and even state staff had trouble directing 

them to someone who could help. Since the state’s system didn’t meet local data 

needs, the district spent its time and money maintaining its own records, while 

grumbling about the state’s shortcomings. Unbeknownst to the districts, however, the state was implementing a data governance process. Gradually, word got around that several local representatives 

were working with the state to improve operations. Districts cheered the first sign 

Book Three of Four: Effectively Managing LDS Data 33 
of change: an email to the districts listing all of the state’s data stewards. Finally, 

district staff knew whom to call if they had questions about a particular program 

area or submission! Next came a data collection calendar accompanied by a notice 

that several of the state’s data collections were being eliminated or pared down 

due to redundancy. District staff gave a collective sigh of relief: not only was some 

burden being lifted, but they finally had an authoritative source of collection due 

dates. Of course, data governance hadn’t solved every problem, but it was quickly 

and noticeably improving operations as well as the districts’ relationships with the 

state. At the state level, goodwill and better data from the districts were confirming 

that their efforts were bearing fruit. 

Federal reporting coordinator 

The federal reporting coordinator oversees the federal reporting activities across the agency 

and, as such, should be involved in the data governance process. Some specific data 

governance responsibilities may include 

 participating in the Data Governance Committee (as a member, not as a data 

manager); 

 providing periodic updates on federal reporting to the Data Governance 

Committee; and 

 working with the Data Governance Committee to address issues related to federal 

data submissions. 

Business analysts 

In essence, the business analyst resides at the border between the business and technology 

sides of the agency and, within the organizational structure, may be in either area. Acting 

as a link between the two domains, the analyst harvests, assembles, and translates business 

needs into foundational technical specifications. One of the agency’s business analysts 

should actively participate in the data governance process. Some specific data governance 

responsibilities may include 

 participating in the Data Governance Committee (as a member, not as a data 

steward); 

 working with Data Steward Working Groups to design the technology component 

of the solutions developed to resolve data issues; and 

 reviewing data element and technology standards. 

34 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Book Three of Four: Effectively Managing LDS Data 35

Business first, then technology

Program areas should lead the data governance process. Good data 

governance forces business staff to think more deeply about their needs. 

If the content area experts don’t think through the data’s purpose, definitions, 

and standards, IT will inevitably make assumptions in order to get the job done.

If their assumptions are wrong—a common occurrence that is not the fault 

of IT—data quality will suffer and undue burden could be imposed on school 

districts. For instance, if a business rule is defined incorrectly (should a student 

who doesn’t earn a high school diploma but enrolls in college be counted as 

a dropout?) or if the option set for an element does not meet business needs 

(allowing “Null” in error, for instance), the resulting data may be problematic 

or even useless. In creating new specifications or amending existing ones, 

program area staff should work out the business solution to the very last detail, 

leaving no room for guesswork. Only then should IT be asked to determine how 

to implement the solution with technology.

Data Governance Groups

The following draws on the Tennessee Department of Education (draft 2008), DQC 2008, Rozelle 

2006 and 2008, Chatis Consulting, and KSDE 2007. 

In addition to the individual roles involved in the data governance structure, several 

groups of policymakers, data managers, and other stakeholders should be convened to 

address data issues collectively. When data issues affect multiple program areas, all affected 

stakeholders should be included to formulate the best response. These groups also help 

foster coordination and shared decisionmaking, ensuring that the agency approaches data 

issues consistently across program areas and over time. 

Data Policy Committee

The Data Policy Committee is a group of executive management stakeholders that may 

include the education chief (or other high-level staff member), the chief information 

officer, the data governance coordinator, and executive leaders from each program area that 

has data stewards. Rather than creating a new committee, it would be preferable to identify 

an existing group that includes these members and insert a standing time slot during 

regular meetings to focus on data governance issues. The relationship between this group 

and the Data Governance Committee (see below) is the critical link between leadership 

and those working directly with the data. The Data Policy Committee provides highlevel sponsorship of the initiative as well as leverage for implementing major data-related 
decisions affecting multiple program areas. This committee also puts the executive “stamp 

of approval” on new or amended policies. Additionally, decisions that are contentious 

or beyond the authority of the Data Governance Committee should be referred to this 

committee for an authoritative resolution. For example, if staff resists a decision made by 

the Data Governance Committee, the Data Policy Committee can provide the authority 

to enforce it. Policies that significantly change the organization’s handling of data and 

data collections should also be discussed by this committee. This relatively small group 

need not meet as frequently as the Data Governance Committee, perhaps once every other 

month. Some specific data governance responsibilities may include 

	 establishing the data governance policy to guide the agency’s efforts; 

	 selecting the data governance coordinator; 

	 establishing the Data Governance Committee; 

	 approving data policies and major data-related decisions referred by the Data 

Governance Committee; and 

	 identifying critical issues to be resolved by the Data Governance Committee. 

Data collection and reporting calendar 

Led by the data governance coordinator, the Data Governance Committee 

should create and maintain a comprehensive, up-to-date calendar of data 

collection and reporting. The calendar should document and detail all current 

and planned data collections, including due dates, descriptions of the data 

and their uses, collection format, and the staff member responsible for each 

submission. This resource should be made available to all staff and to the 

public. 

Additional resources: 

The following resources are available via LDS Share at 

http://nces.ed.gov/programs/slds/LDSShare/SLDS.aspx. 

 Tennessee Data Collection Calendar (2005) 

This is a sample data collection calendar developed by the state of Tennessee. 

 South Carolina Data Collection Manual (2006–2007) 

This manual lists the data elements collected from the School Administrative 

SIS by the South Carolina Department of Education. Also included is a table of 

the various collections, a table describing how the data were used in 2005–06, 

and a table listing the offices in the department that use the data. 

36 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Data Governance Committee 

The Data Governance Committee is the center of control in the data governance structure, 

accountable for the quality of all agency data. It is an enterprise-wide group of data 

stewards chaired by the data governance coordinator. Ideally, the committee will also 

include the chief information officer, the public information officer, the lead business 

analyst, and at least one district representative (though involving multiple districts is 

preferable). Depending on the scope of the desired data system, this group may also 

include representatives from other external organizations, such as higher education; and 

other agencies, such as labor and child services. By including a variety of stakeholders, this 

group facilitates the collaboration necessary to address shared data issues. Meetings should 

occur frequently, perhaps monthly or even more if necessary. An effort should be made 

to make this group act as a resource for data stewards, rather than an obligation. Some 

specific data governance responsibilities may include 

	 creating a clear mission statement to guide the group’s data governance plan (see 

appendix D for a sample mission statement); 

	 working together to identify, prioritize, track, and resolve critical data issues; 

	 identifying the primary, authoritative sources of each data element (where 

multiple sources exist), and determining authoritative definitions and values for 

each data element; 

	 creating a schedule of deadlines for proposed changes to data collections; 

	 approving all new data collections and changes to existing collections—a group of 

district representatives and other affected stakeholders (e.g., other state agencies) 

should be involved in this process of “change management” (see the “Change 

management” box later in this chapter); 

	 creating and maintaining a data collection and reporting calendar; 

	 eliminating the collection of redundant or unnecessary data—agencies may 

conduct periodic sunset reviews to evaluate the continued need for every data 

element it collects; this sizeable task may also be assigned to a separate working 

group (see the “Change management” box later in this chapter); 

	 creating Data Steward Working Groups to address shared data issues that affect or 

involve multiple program areas; 

	 recommending new policies and policy changes concerning the management, 

quality, security, and use of data to the Data Policy Committee (via the data 

governance coordinator); 

	 overseeing the implementation of the work dictated by data-related policies; 

	 establishing standard business rules for data collection, sharing, and reporting to 

streamline operations across the enterprise; 

	 working with the agency’s security team to determine the sensitivity of each 

element and the corresponding level of security required; 

	 referring data issues beyond the committee’s authority to the Data Policy 

Committee (via the data governance coordinator); 

Book Three of Four: Effectively Managing LDS Data 37 
	 facilitating communication and collaboration about data issues across the agency 

between data stewards and other staff, and with districts and other external 

organizations—a standard process for communication should be created and 

followed, including standard form(s) and frequency of communications; and 

	 reviewing and approving standards for data elements with the IT division. 

If the Data Governance Committee does not include much representation from 

districts and other affected stakeholders, the agency may adopt other strategies for 

collecting their feedback. For instance, it may hold in-person or online meetings with 

districts and other affected groups to periodically review proposed data collection changes. 

It is beneficial to include both program area and technology staff from each agency 

in these meetings. Alternatively, a separate group of state staff and affected external 

stakeholders may be formed to take on the responsibility of considering and approving 

proposed changes. 

Data Steward Working Groups 

The Data Steward Working Groups are formed by the Data Governance Committee 

to resolve critical data issues that span more than one program area. Basically teams of 

data stewards, the groups work together to collectively identify the source of each issue 

and develop a solution that addresses each program area’s needs. While all members 

of a working group should contribute to formulating solutions, one steward should be 

responsible for ensuring that the team creates and implements an effective solution. 

(See appendix D for guidelines for Data Steward Working Group activities.) As Chatis 

Consulting explains, specific data governance responsibilities may include 

	 defining and documenting the source (not the symptoms) of the shared data issue 

(communication breakdowns, technology issues, unclear definitions, etc.); 

	 defining the goals of resolving the issue (why the problem should be solved); 

	 creating mini-projects to resolve the problem, including well-documented steps, 

roles, and due dates; 

	 providing monthly updates to the Data Governance Committee; 

	 notifying the data governance coordinator of issues beyond the Data Governance 

Committee’s authority so they can be referred to the Data Policy Committee; 

	 collaborating with IT to implement the business solution to the issue; and 

	 documenting and communicating the solutions to the Data Governance 

Committee. 

38 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Book Three of Four: Effectively Managing LDS Data 

LDS Lore: 

The committee becomes a resource 

When the agency began its data governance initiative, Gary was tapped as his program area’s data steward. He was less 

than thrilled to find out he’d need to attend a monthly meeting 

with other stewards and staff from other program areas. At the 

first meeting, he must have rolled his eyes a dozen times. “What a 

waste of time,” he thought. He had a ton of work to do and needed 

to call one of the districts as its data were on the verge of being late 

for the third cycle in a row. Towards the end of the meeting, Patti, the woman from the English language learner team asked, “Is anyone else having 

trouble with the district over in Stuckeyville?” Gary perked up. “Oh, yeah,” he said. “The guy Steve over there never returns my calls and they’re two days away from their 

deadline.” 

“Forget about Steve. Call Fran. She’ll help you out,” Tara, another data steward, suggested from the other end of the table. The room erupted as more and more participants began sharing stories and offering tips. At the next meeting, one of the stewards, Christina, mentioned “master data management.” “Sorry, but what on earth is master data management?” Tara asked. 

Most sat silently, a few shrugged; finally, Christina explained what she knew about the subject. 

Gary jumped in, “So, it’s kind of like horizontal integration?” 

“I don’t know. What’s that?” Steve asked. 

Yori, the data governance coordinator, suggested they set up a professional development session. It seemed everyone was at least 

a little unclear on the subject. 

Thanks to conversations like these, Gary and the others started looking forward to the meetings, which they now saw as 

a resource, and a sense of camaraderie began to form. Staff from 

previously isolated program areas started helping each other as 

they realized they shared many of the same problems. The Data 

Governance Committee had shown them they were not alone. 

39 
Change management 

While getting a handle on the agency’s data early on in the LDS development process is very important, as time 

goes by, it becomes clear that managing the changes made to the system is also crucial and challenging. As a 

result, some have developed sophisticated approaches to manage these changes. System changes sometimes 

impose additional costs and burdens on districts, and introduce data quality challenges. And poor management 

of change can also be a source of tension between a state agency and its districts. For these reasons, a 

systematic approach to managing change with the involvement of representatives from school districts and 

other affected stakeholder groups is essential for the long-term success of the LDS. 

A change management strategy should ideally include four main steps: 

 Create annual schedules of deadlines for program areas to submit requests for additions or changes to data collections. 

 Establish data governance group(s) to review all proposed changes to the agency’s data collections. Invite a large and 

varied group of district representatives to provide feedback on changes. This local perspective will inform better solutions. 

 Clearly communicate all changes to districts as early as possible. Effective communication strategies include district 

staff involvement in the data governance process; periodic training for regional or district staff and school district vendor 

staff on changes to the data collections such as alterations to record structures, reporting requirements, business 

rules, standards, edits, etc.); Web meetings; and prominent, public online posting of new documentation such as new 

standards, due dates, etc. 

 Conduct periodic sunset reviews to evaluate the need for every data element the agency collects. Justify the continued 

collection of each element and record, considering whether the item is required by state or federal law, or is otherwise 

useful to the enterprise. Eliminating unnecessary data items decreases reporting burdens and increases data collection 

efficiency. 

A lesson from Texas 

The Texas Education Agency has been collecting student-level data for nearly two decades. Early on, the agency realized how 

difficult and costly districts found complying with changes to the state data collection (new data elements, altered codes sets, 

etc.). The state quickly grasped the need to systematically manage the changes by involving districts and other stakeholders in 

the process, and effectively communicating all modifications across the enterprise. 

Putting its plan into action, the state created a data governance structure that includes two groups of external staff focused 

on managing changes to the state’s data collections. One group, the Information Task Force (ITF), includes representatives 

from districts, regional education agencies, and other organizations; and receives all proposed changes to the agency’s data 

collections, including sunsets. Approved changes are referred to the second group, the Policy Committee on Public Education 

Information, which includes representatives from school districts, regional education agencies, and several state agencies (the 

Office of the Governor, Office of the Speaker of the House, the State Auditor’s Office, and others). This committee considers all 

changes recommended by the ITF and either approves, denies, or suggests alternatives to each proposal. 

In addition, every two years, the agency conducts sunset reviews of all of its data elements and records. During these reviews, 

a group of agency data managers and program area staff evaluate the need for each and every data item. The findings are 

presented to the state’s data governance committees with, for each item, a justification for continued collection (including 

descriptions of data usage and any state or federal collection mandates) or a proposal to cease collection. Finally, revised 

standards (definitions, code sets, etc.) and collection requirements (submissions and resubmissions timelines, record layouts, 

edits, etc.) are created and posted prominently on a public website. All changes to collections and resulting state reports are 

also highlighted in periodic training sessions with district, regional, and vendor staff. 

This process has been very effective in reducing district burden, and ensuring that districts and their vendors have ample time to 

prepare for collection changes. 

Source: The above is largely based on input from the Texas Education Agency (TEA), May 2009. For more information, visit TEA’s 

Public Education Information Management System website at http://ritter.tea.state.tx.us/peims. 

40 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Data Request Review Board 

A Data Request Review Board may be formed to manage the data-sharing process and 

to handle data requests, which are likely to increase dramatically once the agency starts 

collecting student-level longitudinal data. This group of directors, legal counsel staff, 

and data stewards (or data owners) should meet monthly. Some specific data governance 

responsibilities may include 

	 creating and enforcing policies and procedures for handling data requests that 

standardize the review of, and response to, data requests (for instance, standard 

criteria for approval and denial, such as legality under privacy laws and potential 

benefits to the educational system); 

	 documenting all approvals and denials of information requests—in addition 

to internal recordkeeping, this allows staff to more easily identify common 

data requests that might be fulfilled through a data mart or other “self-service” 

resource, and it fulfills the recordation requirement under the Family Educational 

Rights and Privacy Act (FERPA); 

	 prioritizing approved data requests based on factors such as merit and staff 

capacity; 

	 referring requests to appropriate data stewards, ensuring consistency in data 

sharing; 

	 monitoring the flow and completion of requests; 

	 referring issues to the Data Governance Committee as needed; and 

	 supporting data-for-fee service—if a request will not benefit the education system, 

or is frivolous or especially time-consuming (for example, the requestor asks the 

agency to reformat existing data to meet their needs), the agency may decide to 

charge a fee to offset the costs of processing the request. 

Whether or not the agency forms this group, clear policies and processes for 

handling data requests should be developed. Establishing standard processes at the onset 

will increase efficiency and help avoid improper data sharing. 

(Kansas State Department of Education 2008) 

Book Three of Four: Effectively Managing LDS Data 41 

 

improving Data Quality 

Part II 

A s education data come to the fore in efforts to improve the education system, 

so too will problems with those data. The return on your LDS investment is 

dependent on the quality of the data maintained in, and available through, the system. This 

reality necessitates a heightened commitment to data quality. 

Bad data can lead to bad decisions. In addition, low quality data will not be trusted and, if 

they are not trusted, the system that maintains them will not be used for better decisionmaking. 

Worse, inaccurate data can send the wrong message, cause misallocation of resources, or 

misdirect interventions. Decisions based on misinformation may have potentially dire 

consequences for individual students, teachers, schools, and districts; and possibly affect funding, 

reputations, careers, and students’ educational opportunities. Fixing bad data ultimately saves 

staff time and resources. If data are of high quality from the moment they are created, the agency 

will be able to process and use the information more rapidly and effectively. 

Poor quality data come from many sources: data entry and reporting errors, confusion 

over which data are the “right” data, and inconsistent or ambiguous standards are all common 

culprits. To avoid costly errors and arm decisionmakers, students, researchers, and other 

stakeholders with timely, high-quality information, education enterprises must strengthen 

strategies for creating and managing data. Data quality should be a high priority throughout the 

agency, with improvement efforts including data governance, clear and enforced policies and 

standards, careful and competent data entry, quality assurance procedures at all levels, and staff 

training and professional development. Staff must not only understand agency data procedures 

and requirements, they must also be convinced of the data’s importance. The records must not 

be seen only as chores; they should be treated as assets that can inform and enhance their work. 

To this end, it is very important that local staff are able to use the data they create. 

The following three chapters aim to help agencies improve the quality of the data they 

create, collect, store, and make available through their LDS. They provide an overview of many 

factors involved in creating quality data, and will direct readers to other resources focused on 

these issues. 

Book Three of Four: Effectively Managing LDS Data 43 

What are Quality Data? 

Chapter 4 

Q uality data are accurate depictions of the real world that are consistent 

across an enterprise, secure and accessible, delivered in a timely manner, 

and suitable for their intended applications (Redman 2001). 

Accuracy and Completeness 

The most obvious measure of data quality is accuracy, or the degree to which the 

information “accurately” depicts the real world construct or phenomenon it represents. 

For example, a student’s numeric grade for algebra I in the LDS must match the one 

printed on his or her report card. To be accurate, data reported and maintained must also 

be complete; for instance, every student must be identified as either male or female. 

Internal Consistency 

Quality data must be consistent across the enterprise. For instance, a student’s name 

should be recorded in the same manner in every silo system. While a particular child 

may answer to “Charles,” “Charlie,” or “Chuck,” only one form of his first name should 

be maintained by the agency. In addition, calculated data items such as the dropout 

rate should be computed the same way if they are calculated more than once. Internal 

consistency may also be referred to as “integrity,” which may be compromised when data 

are somehow corrupted during a data transfer or other process. The concept of consistency 

is also similar to the that of “reliability,” which may be diminished if, for instance, the 

definition of a data element is unclear, leaving room for varying interpretations by the staff 

creating the data. Additionally, data must be coded in a manner that adheres to defined 

code sets. Ultimately, inconsistent data will not be comparable for analysis; nor will they 

be easily interoperable or portable. 

Dimensions of data 

quality: 

• Accuracy 

• Completeness 

• Consistency 

• Utility/validity 

• Timeliness 

• Security 

• Accessibility 

Book Three of Four: Effectively Managing LDS Data 45 
Timeliness 

Quality data must be delivered within a useful timeframe. While a data system may be 

able to provide teachers with accurate student test scores, the data will be of limited use 

if they take months to deliver. Thus, while data may be considered of high quality by 

other measures, they must be accessible to users quickly if they are to meet their intended 

purpose of providing actionable information for decisionmaking. 

Security Yet Accessibility 

Quality data must be secured to protect privacy and to prevent tampering (see chapter 8). 

At the same time, these data must be available to authorized users to provide information 

and improve decisionmaking. 

46 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Data Quality From 

Bottom to Top 

Chapter 5 

T he best way to ensure the quality of data is to get them right in the first place, 

and to prioritize quality throughout the information life cycle. This approach 

relies on staff in the school as well as at the district and state levels. It starts at the source, 

typically in the school where teachers, clerks, and other personnel enter data. From the 

school, the data are sent to the district, where they are validated and/or audited; then to the 

state agency and federal government, where further quality assurance processes take place. 

The Forum has more detailed information... 

...about improving data quality: 

• Forum data quality online courses 

http://www.academypa.org/sifa/splashdq.html 

• Forum Curriculum for Improving Education Data: 

A Resource for Local Education Agencies (2007) 

http://nces.ed.gov/forum/pub_2007808.asp 

• Forum Guide to Building a Culture of Quality Data: 

A School and District Resource (2004) 

http://nces.ed.gov/forum/pub_2005801.asp 

Book Three of Four: Effectively Managing LDS Data 47 
In addition to the processes that check the flow of data “up” the ladder from the 

school, quality also relies on effective governance and communication back “down” again. 

Establishing effective data governance at the state and district levels provides a mechanism 

to help resolve problems and prevent finger-pointing or issue avoidance. Education 

agencies must move from ad hoc data management models to strategies that bring together 

all stakeholders from across the enterprise, create key governance groups, assign clearly 

defined roles and responsibilities, secure agency data, and ensure the data help achieve 

organizational goals. (For more information on data governance, see chapters 1–3.) 

State agencies and the federal government also establish policies, guidelines, 

standards, and reporting requirements that must be effectively communicated “down” 

to the data suppliers to enable successful and timely implementation at the local level. 

Likewise, school districts may create their own standards, guidelines, policies, and 

regulations to guide school data activities. They create data reporting calendars, data 

dictionaries, metadata systems, and business rules; assign responsibilities; and implement 

technology to facilitate data processes. These guidelines and procedures should be similar 

across program areas so that schools will have comparable experiences submitting various 

data. In other words, submitting data to one program area should not be very different 

from submitting to another. 

Responsibility for data quality should ultimately rest with program area staff, 

rather than information technology staff. While IT can perform basic checks to see if 

the data “look” right, program area staff have a deeper understanding of the information 

and are better equipped to find errors. For instance, a data report might look right to 

IT if a total number of schools is generated, but a program area staffer may know if that 

number is actually correct. This in no way means that IT staff and the technology they 

manage are not critical factors in improving data quality. Technology that streamlines and 

automates data entry and sharing is indispensable to quality, as are validation procedures 

implemented through technology. However, to ensure data quality, education agencies 

often focus too much on technology and not enough on the data or the people and 

business processes regulating them. If reported data are inaccurate from the start, the best 

technology solutions will fail to transform them into quality data. 

While the state 

education agency 

can do several 

things to improve 

the quality of its 

data, it is essentially 

a data “receiver,” 

relying on school 

and district staff 

to provide quality 

information. 

Data quality certification 

The Kansas State Department of Education has been a leader in improving data 

quality at the local level. The state has created a professional development 

program that trains and certifies a range of school and district staff on data 

quality practices and techniques, as well as software applications. For more 

information on Kansas’s Data Quality Certification program, visit 

http://www.ksde.org/dqcprogram. 

48 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Provide Training and Professional Development 

At the local level, where the life cycle of information begins, the data “creators”—the school 

teacher, counselor, nurse or secretary entering student data to the district or regional service 

agency; or vendor staff member building a report for the state agency—must be trained 

to ensure they produce high-quality data. This training should include best practices and 

procedures for creating and entering data; and the use of the technology employed to 

collect, edit, and report data. Staff should also be very familiar with all relevant policies, 

data standards, reporting requirements, and timelines. 

Data appreciation leads to better data quality 

Staff preparation should teach more than policies and procedures. Professional 

development programs, and ongoing communications throughout the enterprise, should 

help everyone understand why data are so important. Staff need to know how their 

handling of data affects the use of those data at all levels, shaping decisions from school 

funding to individual student learning. They must understand why the data are collected, 

how teachers and decisionmakers use the information, and how the work relates to the 

money the school or district receives. Understanding their uses will help staff appreciate 

why data must be accurate and timely, and provide an incentive to strictly adhere to 

procedures. 

Data quality results from data use 

If staff see data collection and reporting simply as chores to perform for an authority, 

they may not be sufficiently motivated to go the extra mile to ensure quality. To create an 

incentive to improve data quality, agencies must ensure data are used down to the school 

office and classroom levels. For instance, data will be more useful to practitioners if they 

have access to student-level data with reporting and analysis tools, or dashboards. District 

administrators can access the data to see how their schools compare with similar districts in 

the state. Teachers can view data in real time to inform lesson plans and tailor instructional 

strategies. Additionally, state agencies may turn the submitted data into useful reports 

for schools and districts perhaps enrollment lists or comparisons with other schools and 

districts. If data creators see that the data are used for high-stakes calculations to make their 

jobs easier, or to hold them accountable, they will have greater incentives to ensure the 

data are of high quality. 

Data Auditing Procedures 

The flow of data from schools and districts to the state LDS should include several 

safeguards to ensure quality. For example, on the way from the school secretary to the 

district and on to the state data system, certain procedures and mechanisms should be in 

place to check the data’s quality, identifying and resolving anomalies. Ideally, data should 

be checked for quality before they are loaded into the collecting systems. Some states use 

auditing mechanisms to check submitted data for problems, and validation reports to 

alert staff if they find any. Audits may include, but are not limited to, the application of 

business rules that 

Book Three of Four: Effectively Managing LDS Data 49 
	 compare data to prior year values to spot any significant changes where little or 

no change is expected (e.g., a change in a student’s race); 

	 identify invalid values (e.g., “Null” in a field that requires a numeric value, invalid 

codes, incomplete or blank fields, out-of-range or over-limit values); 

	 identify invalid formats (e.g., a date entered in incorrect format); and 

	 detect excessive use of certain codes (e.g., the frequent use of “Other”). 

Such front-end validation procedures are preferable to back-end cleansing of data 

already loaded into the system. In addition, errors should ideally be corrected in the 

source files and resubmitted, rather than amended in the state or district’s system. If a 

correction must be made by state or district staff, a process should be in place to ensure 

that the source files are also corrected (Schutte et al. 2009). Errors may be identified as 

critical, which would require correction; or noncritical, which might require staff review, 

but not necessarily correction. Data suppliers should be notified of these discrepancies 

and be required to correct errors or confirm that any questionable data are, in fact, correct. 

Agencies must determine how often their data will be validated, and establish timelines for 

submissions and resubmissions. To further validate the information, some states also verify 

reports with district program area staff before the data are finally released to ensure that 

the numbers match districts’ expectations. This type of process helps guarantee the data 

reported are accurate representations of the reality. 

50 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Speaking the Same language: 

Data “Standards” 

Chapter 6 

Data standards are documented agreements on representations, 

formats, and definitions of common data; and are intended to 

improve the quality and share-ability of education data. 

A look around the country reveals that many states and districts are building 

separate and dissimilar LDSs. While many see this as problematic, 

potentially complicating the exchange and comparison of data in the systems, trouble 

actually only arises when the different systems use incompatible standards. Troublefree data sharing within an agency and with external systems will largely depend on the 

standardization of data elements and technical specifications across systems, as will the 

quality, comparability, and usefulness of the data. Adherence to common data standards is 

the key to bridging these systems, achieving interoperability, and enabling analysis across 

institutions. 

Data standards are documented agreements on representations, formats, and 

definitions of common data elements; and are intended to improve the quality and shareability of education data. Under the umbrella of data standards, three major types of 

standards serve disparate audiences and purposes: 

	 Data definitions and code sets 

 Definitions and code sets are concerned with the meaning and content (e.g., 

values) of data elements. In a sense, these standards provide a common 

vocabulary or language for anyone who manages or uses education data. 

Sometimes called “suggested standards,” they are typically included in data 

dictionaries, glossaries, and various other resources. These standards may be 

Book Three of Four: Effectively Managing LDS Data 51 
useful to a broad array of users, including teachers, district and state data staff, 

researchers, institutions of higher education, and private sector organizations. 

	 Technical specifications 

 Technical specifications are used by software and systems developers to facilitate 

interoperability between applications or to guide data reporting. They typically 

provide technical criteria or requirements, methods, and processes for data 

reporting and management such as field length or data type. Technical standards 

are useful to software application vendors and system developers, as well as 

education agency staff who submit data to a collecting agency. 

LDS Lore:
 

What’s a school anyway?
 

Unexpected difficulties may arise during the development of standards. For instance, one state agency 

spent months defining “school,” a task that no one had 

anticipated would be particularly difficult. Staff wrestled 

with the nuances. Should special education schools be 

included? How about private schools? Could two schools 

reside in a single building or one school span across 

multiple buildings? The choices were anything but obvious. 

Variations in definitions like these can have serious 

implications, including on funding allocations. 

	 General guidelines/relationships 

 General standards describe the relationships between data elements. Data 

models are the typical source of such information. These models are commonly 

developed for software and systems developers to help build data system 

architecture, as well as for researchers who need to explore the types of data 

available to them for study. 

Use Common Definitions and Codes 

Adherence to common data definitions and codes facilitates comparability, 

interoperability, and portability within and among K–12 institutions; as well as with 

early education, postsecondary, and workforce organizations. Without a standard set of 

common data elements, there would be no way to make sense of the data collected and 

shared across schools and districts, or to truly follow students over time as they change 

52 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
 

schools and districts. In addition, unless states adopt the same data definitions and coding 

systems, drawing valid comparisons among states will be difficult if not impossible, and 

will, at a minimum, require the time-consuming process of crosswalking the data elements 

from one state system to another state system. 

District difference 

Many districts have their own systems for coding data elements such as 

“course.” Local education agencies may either adopt the state’s system 

or School Codes for Exchange of Data (SCED), or map existing local 

codes to the state or SCED ones, an approach that may be less laborintensive than changing standards. 

For instance, if one state counts as “graduates” students who dropped out of high 

school, but later earned a GED or enrolled in a postsecondary institution, that state’s 

cohort graduation rate may be higher relative to a state that considers such students 

“dropouts.” Common codes for recording education data (academic courses, exits, 

attendance, etc.) or, alternatively, the crosswalking of local codes to a common system 

offers many benefits. The establishment of common data codes like those for race and 

ethnicity or for courses makes it easier to transfer information and draw comparisons 

across entities. In the case of course codes, for instance, a universal system can reduce time 

spent interpreting course information from transfer schools and help place students in 

the appropriate courses when they switch schools in the state or even across the country. 

For example, common course codes based on academic standards taught in each course 

will ensure that a student who completed algebra I in one district will be placed in the 

appropriate follow-up course in any school across the state. Use of common course 

codes also allows more reliable comparisons of performance data over time and across 

institutions. For example, an analysis considering the effects of taking algebra I will yield 

reliable results only if the courses measured are comparable in content—in other words, 

if they were classified by a common course-coding system based on academic standards 

taught in each course. 

Maintain Metadata 

Metadata, or “data about data,” are critical to guiding proper data management and 

informed use. Without an organized approach to recording information about the agency’s 

data elements, and without governing standards, the staff must remember or otherwise 

track all the information necessary to understand the data. This might include definitions; 

technical specifications such as field length and format, data source, due date(s), purpose, 

business rules, related calculations or transformations, and related policies; and all other 

information relevant to the creation, management, interpretation, and use of those data. 

In today’s environment, where data elements are numerous, complex, and ever-changing, 

managing a data system without a robust metadata system would not be practical or 

advisable. In fact, many education agency staff consider a central, authoritative metadata 

repository a critical component of effective management and use of an agency’s data. 

Book Three of Four: Effectively Managing LDS Data 53 
 Data models 

Adata model documents the agency’s data architecture, helping users make sense of the many items that may be tracked by the system. By presenting an 

inventory of all individuals, places, and other entities involved in education, and 

by describing the relationships among them, an education data model can help 

education institutions, vendors, and researchers better understand the education 

data environment. Thus, a data model can help vendors and agency staff in data 

system design, or assist agency leaders select a product on the market that will 

meet their stakeholders’ needs. A data model may also be a resource in the 

search for data to build into research designs. 

While developing their data models, states and districts should focus 

on program area needs. IT should implement the model, but the business side 

of the organization should drive its design. Data models are offered by several 

vendors and by the federal government, which recently developed and continues 

to enhance a national data model called the National Education Data Model 

(NEDM). This nonproprietary model is available at 

http://nces.ed.gov/forum/datamodel/index.aspx. 

The Forum has more detailed information... 

...about metadata and metadata systems: 

Forum Guide to Metadata: The Meaning Behind Education Data (2009) 

http://nces.ed.gov/forum/pub_2009805.asp 

54 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Standards instability 

Standards are constantly altered as data meanings are refined, institutions 

seek to align with other agencies to facilitate data sharing, new information 

is desired, new collection requirements are imposed, populations evolve, 

and problems with existing standards are identified. While this evolution is 

necessary and good, data quality and efficient use of resources demand that 

states document and make public all changes to their data standards so that 

suppliers have enough lead time to comply (see the “Change management” 

box in chapter 3). 

National Standards Resources 

State and local education agencies use a variety of data “standards,” be they locally 

developed or adopted from state or national sources. Whatever the source, these standards 

are commonly set to meet federal and state data reporting requirements. However, shared 

data standards across the education community offer many other benefits. For instance, 

they enable interoperability, eliminating redundant data entry, lessening reporting burdens, 

reducing data errors, and facilitating data transfers. Using shared data standards also 

enables portability of student data and transcripts (see chapter 10 of Book Two: Planning 

and Developing an LDS); and common adherence to shared data standards allows valid data 

comparisons across district and state lines. 

Education agencies may refer to several major sources for national data standards 

when designing, buying, adjusting, or using a data system. Figure 4 presents the three types 

of standards described above. Examples of what each type of standard looks like in reality 

are presented in the second column. 

Common Education Data Standards Initiative 

The Common Education Data Standards (CEDS) Initiative is a national, collaborative 

effort to identify a set of data elements of particular importance in K–12 education and 

the transition to postsecondary education. The CEDS Initiative has identified key data 

elements describing demographics, program participation, course information, and other 

attributes of students and the education system, as well as elements needed for high 

school-to-postsecondary transcripts and high school feedback reports. For each of these 

elements, the CEDS Initiative has defined detailed data standards including definitions, 

code sets, and a range of technical specifications. Most of these data standards have 

been drawn or adapted from established sources such as the NCES Handbooks, Schools 

Interoperability Framework (SIF) Association specifications, and Postsecondary Electronic 

Book Three of Four: Effectively Managing LDS Data 55 
56 Traveling Through Time: The Forum Guide to Longitudinal Data Systems

Figure 4. Data standards examples by type

Data definitions Hispanic or Latino Ethnicity = An indication that the individual traces his or her origin or descent 

and code sets to Mexico, Puerto Rico, Cuba, Central and South America, and other Spanish cultures, regardless 

of race. The term “Spanish origin” can be used in addition to “Hispanic or Latino.”

Code set:

 02304 - Hispanic or Latino

 02305 - Non-Hispanic/Latino

 (Source: NCES Handbooks, Version 7.0)

Technical 

specifications

General 

guidelines and 

relationship

Element/@Attribute Char Description Type

HispanicLatino An indication that the Values:

individual traces his or Yes

her origin or descent to No

Mexico, Puerto Rico, Cuba, 

Central or South America, 

or other Spanish cultures, 

regardless of race. 

Table 6.2.32-1: HispanicLatino

<HispanicLatino>Yes</HispanicLatino>

Example 6.2.32-1: HispanicLatino

(Source: SIF Specification, Version 2.3)

 RaceList ?

 RaceList type

 HispanicLatino ?

 HispanicLatino type

 Demographics

 Demographics type

 Gender ?

 Gender type

 CountryOfBirth ?

 Country type

(Source: SIF Specification, Version 2.3)
Standards Council (PESC) schema; and will be incorporated into the National Education 

Data Model (NEDM). Ideally, this core set of data elements will be voluntarily adopted 

by education agencies and marketplace providers. These sources are described on the 

following pages. 

For more information on the CEDS Initiative, visit http://commondatastandards.org. 

EDFacts initiative 

EDFacts is a data initiative of the U.S. Department of Education compiling national, 

K–12 education data by consolidating previously separate federal collections. By 

combining these collections, EDFacts aims to centralize performance and other aggregate 

data for decision- and policymaking in order to streamline submissions to the federal 

government and eliminate redundancies, thus easing the burden on state agencies. Data 

collected for EDFacts include student and staff demographics, program participation, 

student performance and completion, school and district directory data, revenues and 

expenditures, school choice options, and other information. As a standards resource, 

EDFacts provides data elements, definitions, and code sets. The Department of Education 

also publishes technical specifications for EDFacts to guide the file submission process. 

Since much of the data collected by states are used to meet federal reporting requirements, 

the standards provided by EDFacts are commonly adopted by the states to facilitate 

compliance. In fact, all of its data elements have been incorporated into the National 

Center for Education Statistics (NCES) Handbooks, the Schools Interoperability Framework 

(SIF) specifications, and the National Education Data Model (see below). States submit 

their EDFacts data to the U.S. Department of Education through The Education Data 

Exchange Network (EDEN). 

For more information on EDFacts, visit http://www.ed.gov/about/inits/ed/edfacts/ 

index.html. 

NCES Handbooks 

The NCES Handbooks include a vast collection of basic data elements and option sets. The 

resource’s stated purpose is “to provide a comprehensive listing of all data elements that 

might be needed for decisionmaking related to managing an education system, reporting 

to state and federal education agencies, and computing indicators of school effectiveness.” 

Data elements are updated annually and organized into seven “domains” or levels: class, 

intermediate educational unit (IEU), local education agency (LEA), school, staff, state 

education agency (SEA), and student. For each data element, a definition is provided along 

with an option set if applicable. As a standards resource, the Handbooks offer a catalog 

of data elements, definitions, and code sets that is consistent with all the data elements 

needed to submit to the EDFacts data collection. They also include the School Codes for 

the Exchange of Data (SCED) and several standards provided by Forum publications (see 

below). Additionally, most of the Handbooks’ terms, definitions, and code sets have been 

incorporated into the SIF specifications and the NEDM (see below). 

For more information and to access the NCES Handbooks Online, visit 

http://nces.ed.gov/programs/handbook. Education agency administrators may also use the 

Handbooks’ Data Dictionary Customization site to build their own data dictionaries. 

The Handbooks include the Secondary School Course Classification System: School 

Codes for Exchange of Data (SCED), which presents a course taxonomy and course 

Book Three of Four: Effectively Managing LDS Data 57 
descriptions for secondary education.* These codes are specifically intended to help 


education agencies track students longitudinally as they advance through grade levels, 


transfer to different schools, or enroll in a postsecondary institution. (See appendix C for 


additional information on the SCED.)
 

National Education Data Model 

The National Education Data Model (NEDM) provides general data guidelines, depicting 

the relationships between a large collection of data elements collected and used in 

P–12 education. Specifically, NEDM focuses on the granular data items, attributes, and 

relationships associated with teaching, learning, and business operations at the school and 

district levels. For instance, the data model will tell you that a student with a specific name, 

physical address, phone number, displacement status, and other attributes, receives services 

from a teacher and participates in a class that has a room number within a building, which 

is a capital asset defined in the NCES Handbooks, and so on. Version 2 of the data model 

can be accessed at http://nces.ed.gov/forum/datamodel/index.aspx. The model currently 

includes all of the elements contained in the NCES Handbooks, and overlaps considerably 

with the data elements in the SIF specifications (see below). Several Forum standards are 

also included (see box below). In the future, NEDM is also expected to include elements 

from postsecondary education that are included in the Postsecondary Electronic Standards 

Council standards (see below).

 National Forum on Education Statistics 

T

he National Forum on Education Statistics (the Forum) has long been a 

leading resource for education data standards, focusing on issues of data 

standardization and basic data elements. A group of state and local education 

agencies, the federal government, and other organizations, the Forum has 

produced several guides that provide voluntary, best practice recommendations 

about data standards, including definitions, codes, and education data system 

components. To date, these products have covered areas such as crime, violence 

and discipline, attendance, exits, finance, facilities, and student displacement. 

Many of the Forum’s standards have been incorporated into the NCES Handbooks, 

the NEDM, and the SIF specifications. For more information on the Forum’s 

publications, see appendix F. 

*A similar collection of course codes for the elementary and middle school levels is currently being developed by 

the Forum. For more information, see the Forum’s Prior-to-Secondary School Course Classification Working Group 

at http://nces.ed.gov/forum/emscourseclass.asp. For a basic list of elementary-level course codes in the NCES Handbooks 

Online, see the related options list for the NCES Handbooks data element “Elementary Course/Subject” at 

http://www.nces.ed.gov/programs/handbook/elementinfo.asp?elementid=5491.

58 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Postsecondary Electronic Standards Council 

The Postsecondary Electronic Standards Council (PESC) is an organization of colleges 

and universities; professional and commercial organizations; data, software, and service 

providers; nonprofit organizations and associations; and state and federal government 

agencies. Among the organization’s missions is to create data standards to facilitate 

the exchange of data among postsecondary institutions. As a standards resource, PESC 

provides a range of standards for higher education, listing data elements, definitions, 

and code sets; and specifying technical requirements. The PESC standards for student 

transcripts have been crosswalked to the SIF specifications for student records to ensure 

comparability and completeness. However, because the standards are implemented 

differently, some variations exist and the two organizations continue to work together to 

ensure interoperability. PESC data elements related to student transitions to postsecondary 

education, such as e-transcript information, will also be included in the NEDM in the 

future. To enable data sharing with postsecondary institutions, K–12 education agencies 

may use PESC standards about students bound for, or enrolled in, higher education. 

For more information on PESC, visit http://www.pesc.org. 

Schools Interoperability Framework Implementation 

Specifications 

The Schools Interoperability Framework (SIF) Association is a nonprofit organization 

whose members include local and state K–12 agencies, software vendors, and others in 

the education community. The organization has created and continues to enhance a 

vendor-neutral “technical blueprint” for exchanging K–12 data. As a standards resource, 

SIF offers a full range of standards and defines suggested standards for naming, defining, 

and formatting data elements; as well as the technical specifications to allow software 

applications from different developers to easily interact and exchange data. SIF also 

includes a data model that depicts the relationships among the data; and data elements 

in areas such as student information, assessment, facilities, finances, food services, 

transportation, and professional development. The SIF specifications incorporate the 

NCES Handbooks elements and code sets whenever possible. All of the EDFacts elements 

are also included, and the SCED codes are referenced, as well as other standards provided 

in Forum products. SIF also overlaps with PESC standards related to transitions to 

postsecondary education, such as student transcript information. 

For more information on the SIF specifications, visit http://www.sifinfo.org/us/index.asp. 

Book Three of Four: Effectively Managing LDS Data 59 

Securing the Data, 

Protecting the individual 

Part III 

A s state and local education agencies implement LDSs, the collection, 

management, and dissemination of individual student records will increase 

the need to protect individual privacy and dramatically raise the stakes for data security. 

While many state agencies, districts, and schools have not maintained student-level 

longitudinal data in the past, many have handled extensive and sensitive records on 

individual students and staff. And even the aggregate data sets maintained at the district 

and state levels may contain information that can be used to single out individual students, 

thus requiring special protection. 

A number of federal and state laws and related regulations exist to protect the 

privacy of individuals, and these laws and regulations must be followed. Education 

agencies must also create and implement their own policies and procedures to guide staff 

in accordance with these laws and regulations, and these policies and procedures should 

be reinforced with staff training and technology solutions to protect sensitive records. The 

final two chapters address these issues. 

Book Three of Four: Effectively Managing LDS Data 61 

Privacy and Confidentiality 

Chapter 7 

Privacy ≠ Confidentiality 

Though often confused, there is a distinction between privacy and 

confidentiality. “Privacy refers to an individual’s right to withhold information, 

that is, not to divulge information to anyone else. Confidentiality refers to the 

handling of information that has been obtained by a second party.” 

(National Postsecondary Education Cooperative 1998) 

T o reach their potential, LDSs must be used to collect, maintain, and 

make student- and staff-level data available to a wide variety of audiences. 

Teachers, students, principals, legislators, researchers, postsecondary administrators, and 

others can benefit from access to longitudinal data (see chapter 5 of Book One: What 

is an LDS?). However, while these data can greatly enhance the ability to efficiently 

allocate resources and improve programs, instruction, and achievement, the sensitivity of 

personally identifiable information and the need to protect it cannot be overstated. 

Individual privacy must be safeguarded in compliance with federal and state laws and 

regulations; and unauthorized and unlawful access must be prevented. Procedures should 

therefore be developed to allow secure and appropriate data sharing with organizations 

and users throughout the education community and beyond. While there has been debate 

and uncertainty over how best to protect privacy without limiting research and data access, 

many states have demonstrated that an effective balance can be struck. This chapter 

provides a basic overview of issues and relevant laws about data protection. 

Book Three of Four: Effectively Managing LDS Data 63 
64 Traveling Through Time: The Forum Guide to Longitudinal Data Systems

Federal Privacy Laws

Don’t take it from us!

Information offered here on these federal laws should not be considered legally 

binding interpretations. Given the complex and dynamic nature of these laws, 

specific questions about student record confidentiality should be referred to the 

appropriate federal office (e.g., the Family Policy Compliance Office), or your 

agency’s legal or administrative agents. For additional resources on privacy 

issues, see appendix C.

This chapter provides brief overviews of the four key federal laws that directly affect the 

data collected and maintained by education agencies. These are the Family Educational 

Rights and Privacy Act (FERPA), which applies to the vast majority of education data; and 

the Health Insurance Portability and Accountability Act (HIPAA), the Individuals with 

Disabilities Education Act (IDEA), and the National School Lunch Act (NSLA), which 

apply to education data in some cases. Though the details of these laws and their official 

interpretations do not spell out every detail, they do provide basic guidelines on what data 

can be shared, with whom, and under what circumstances. State policies and laws often work 

out some of the implementation issues, and sometimes add further privacy protections.

Personally identifiable information

Before reviewing the privacy laws of import, which data are affected should be clarified. 

These privacy laws put no restrictions on data sharing if all individually identifiable 

information is removed from the records. According to the FERPA regulations, “personally 

identifiable information” includes, but is not limited to, the following:

a. the student’s name;

b. the name of the student’s parent or other family members;

c. the address of the student or student’s family;

d. a personal identifier, such as the student’s Social Security number, student 

number, or biometric record;

e. other indirect identifiers, such as the student’s date of birth, place of birth, and 

mother’s maiden name;

f. other information that, alone or in combination, is linked or linkable to a specific 

student that would allow a reasonable person in the school community, who does 

not have personal knowledge of the relevant circumstances, to identify the student 

with reasonable certainty (see below); or

g. information requested by a person who the educational agency or institution 

reasonably believes knows the identity of the student to whom the education 

record relates.

Source: Federal Register (Dec. 9, 2008) 
In some cases, even when the more obvious personal information is removed from 

individual student records, users may still be able to match individuals to their records 

when those students’ characteristics are rare or unique. For example, the only female Asian 

3rd grader enrolled in a school will be easily identifiable in a data set, even if all of the 

obvious personally identifiable information is removed from her record. In cases like these, 

state and local staff need to take a proactive approach to preventing such invasions of 

privacy, using techniques such as perturbation, encryption, redaction, or deletion of data 

to maintain the confidentiality of private information. 

NCES has more detailed information... 

...about protecting education data: 

• Basic Concepts and Definitions for Privacy and Confidentiality 

in Student Education Records (2010) 

http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2011601 

• Statistical Methods for Protecting Personally Identifiable 

Information in Aggregate Reporting (2010) 

http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2011603 

• Data Stewardship: Managing Personally Identifiable Information 

in Student Education Records (2010) 

http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2011602 

Agencies may also need to manipulate aggregate data sets or performance reports that 

include groups of less than a specified number of students—5 or 10, for example—to avoid 

exposing an individual student’s score or other personal information. In practice, agencies 

may choose to suppress all the information about a small subgroup, or combine subgroups 

to raise the number or percentage of students reported in a group. This minimum n should 

be large enough to protect privacy and ensure statistical reliability, while also avoiding the 

loss of too much detail (ESP Solutions 2008). Similarly, agencies should also manipulate 

their data sets or reports if certain percentages are too large. For instance, if 100 percent of 

students in a school are eligible for free or reduced-price meals, users will know the eligibility 

status for every student in that school. In this case, the percentage may be artificially 

decreased to create uncertainty about who is eligible and, thus, protect students’ privacy. 

Book Three of Four: Effectively Managing LDS Data 65 
The Forum has more detailed information... 

...about FERPA and HIPPA: 

• Forum Guide to the Privacy of Student Information: 

A Resource for Schools (2006) 

http://nces.ed.gov/forum/pub_2006805.asp 

• Forum Guide to Protecting the Privacy of Student Information: 

State and Local Education Agencies (2004) 

http://nces.ed.gov/forum/pub_2004330.asp 

• Privacy Issues in Education Staff Records (2000) 

http://nces.ed.gov/forum/pub_2000363.asp 

Visit the Forum’s FERPA resources page at http://nces.ed.gov/forum/ferpa_links.asp. 

Note: A revised Forum publication on privacy is being developed. 

Anonymized data are 

previously identifiable 

data that have been 

de-identified and for 

which a code or other 

link no longer exists. An 

investigator would not be 

able to link anonymized 

information back to a 

specific individual. 

(http://healthcare. 

partners.org/phsirb/ 

hipaaglos.htm) 

66 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 

Once personally identifiable information has been removed or manipulated as 

necessary, the resulting anonymized data may be shared with the public without consent 

according to FERPA. However, some states restrict access even to these anonymized data 

to varying degrees. 

Family Educational Rights and Privacy Act 

The Family Educational Rights and Privacy Act of 1974, commonly referred to as 

FERPA, is a federal law intended to protect the privacy of student education records. 

The law applies to all education institutions that receive federal funding under programs 

administered by the U.S. Department of Education. 

FERPA has increasingly become an important issue in the education community, 

especially recently because of the emerging implications of LDS development and data 

sharing. As a result of the rapid advance of technology and the expansion in data collection 

and demand, a rising level of uncertainty has surrounded the law’s implementation. FERPA 

generally prohibits agencies from sharing personally identifiable information without written 

consent (though a number of exceptions are made), and many agencies have been reluctant 

to share data in some instances for fear of infringing on their students’ rights. While this 

hesitancy is often justifiable, in some cases agencies may be overly cautious and withhold 
information based on too strict an interpretation of the law. This roadblock to data access 

has been a continuing source of frustration for many potential users, primarily education 

researchers. And, it might be possible to use FERPA as an excuse not to release data that 

might portray the education system in an unfavorable light (Viadero 2006). 

Written when most individual education records were maintained on paper at the 

local level, FERPA’s authors did not consider modern electronic records or statewide LDSs. 

To keep up with the evolution of technology and culture, the U.S. Department of Education 

has offered subsequent interpretations of FERPA, allowing the education community to 

progress while still honoring the law. In 2008, for example, a revised interpretation of 

FERPA was issued to clarify many of the ambiguities and remove some of the roadblocks 

that existed in previous regulations. Of major significance were expanded disclosure rights 

to state education agencies, effectively paving the way for easier access to statewide studentlevel data (previously, only districts were granted disclosure rights, a limitation that, among 

other concerns, hindered researchers seeking to compile significant samples of student 

data). Additionally, the new regulations refined guidance concerning disclosure of student 

information to parents, third parties, former schools, state auditors, and research institutions; 

recordation (recordkeeping for each disclosure); data sharing among K–16 education 

institutions; de-identification of shared records; and the use of Social Security numbers. 

While the new regulations were intended, at least in part, to strike a balance between 

the protection of student privacy and the facilitation of valuable research, questions about 

the law remain. These uncertainties center primarily on the particulars of sharing P–12 data 

with researchers, postsecondary institutions, students’ former schools or districts, and other 

state agencies such as workforce and social service agencies (Education Counsel 2008). 

Further clarifications may be necessary to reconcile the law with the federal government’s 

goal of fostering the development and effective use of statewide, student-level LDSs. For 

more information on FERPA, visit http://www2.ed.gov/policy/gen/guid/fpco/index.html. 

Health insurance portability and accountability act 

The Privacy Rule of the Health Insurance Portability and Accountability Act of 1996 

(HIPAA) is intended to protect the confidentiality of individual health records. In general, 

elementary and secondary schools and districts are not subject to HIPAA, because even 

if they qualify as a “covered entity,” any health-related data they maintain are considered 

“education records” subject to FERPA. FERPA takes precedence even for records created 

by school nurses or other healthcare providers, if they are under the direct control of the 

school. Most schools and districts must comply with HIPAA only when they request 

medical records from an outside health care provider. Once those data are received from 

the outside health care provider and in the education institution’s possession, they are 

considered education records and become subject to FERPA. Private schools that do not 

receive funding from the U.S. Department of Education are the most common exception. 

In this case, any health-related data about students or others who receive health care 

services are considered “protected health information” and must be protected in 

compliance with HIPAA. For more information on HIPAA, visit http://www.hhs.gov/ocr/ 

privacy/index.html. 

Individuals with Disabilities Education Act 

Records on students in special education programs are subject to the privacy requirements 

of the Individuals with Disabilities Education Act (IDEA). The IDEA requirements include 

many of the same protections provided by FERPA, with a few differences related to the 

handling of student records and several additional requirements. For instance, information 

Book Three of Four: Effectively Managing LDS Data 67 
on a student’s disability cannot be shared without parental consent. Institutions subject to 

both FERPA and IDEA must comply with the privacy provisions of both laws. Considerable 

overlap between the laws simplifies this task. For more information on IDEA, visit 

http://www2.ed.gov/about/offices/list/osers/osep/policy.html. 

National School Lunch Act 

Data on students’ eligibility for free and reduced-price meals, and information obtained 

as part of the National School Lunch Program of the U.S. Department of Agriculture, are 

covered by confidentiality restrictions in the National School Lunch Act (NSLA). While 

also subject to FERPA, the privacy restrictions of the NSLA are stricter in two cases: free 

and reduced-price meal eligibility. The sharing of individually identifiable information 

obtained during the eligibility process is, with some exceptions, prohibited without 

parental consent. However, in some cases, eligibility and other information about the 

student’s household may be shared with select individuals and programs, such as some 

assessment programs (e.g., the National Assessment of Educational Progress). In most 

states, though, these data may be made available to users if all personally identifiable 

information has been removed. For more information on NSLA, visit 

http://www.fns.usda.gov/cnd/lunch. 

State Laws 

Many states have established their own laws and policies that either mirror, or expand on, 

the basic guidelines provided by federal laws. For instance, some states have issued laws 

dealing with areas within FERPA they considered ambiguous. They may, for example, 

have defined authorized disclosures more specifically, established a process for approving 

disclosures through written agreements, specified roles and responsibilities for protecting 

privacy, or allowed the use of Social Security numbers as student identifiers. Other 

states have passed laws that explicitly permit certain data sharing between the K–12 and 

postsecondary sectors, among state education agencies, or with other state agencies such as 

workforce or social service agencies. On the other hand, some states have enacted laws that 

are more stringent than the federal laws. For instance, they may prohibit data sharing that 

would be permitted under the current interpretation of FERPA, such as disclosures from 

the state education agency to districts receiving a transfer student, or to teachers about 

their students. To ensure a balance is struck, states should review their existing privacy 

laws, regulations, and guidelines so that they will not inhibit effective use of the studentlevel data they intend to make available through their LDS. 

(Sources: DQC 2007, Hill 2008, and Nunn et al. 2006) 

68 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Data Security 

Chapter 8 

W hether or not an education agency has an LDS, data need to be secured 

to prevent unauthorized access and tampering. However, the collection, 

maintenance, and dissemination of student-level data via an LDS and other source systems 

increases the importance of data security. While many districts have long stored some 

personally identifiable information, states take on a new responsibility when they begin to 

manage personally identifiable, student-level data. 

The Forum has more detailed information... 

...about data security issues: 

• Forum Unified Education Technology Suite (2005) 

http://nces.ed.gov/forum/pub_tech_suite.asp 

• Forum Guide to Decision Support Systems: A Resource for Educators (2006) 

http://nces.ed.gov/forum/pub_2006807.asp 

Book Three of Four: Effectively Managing LDS Data 69 
Security measures should keep sensitive data out of the wrong hands, while allowing 

maximum accessibility to users. An LDS contains sensitive data that can be compromised 

and used to expose restricted personal information, thereby violating privacy (Houde 

2008). Protections must allow access to authorized users, while barring others from seeing 

or manipulating the data. 

Know your data

 Ownership model of data security 

Within a data governance structure, ultimate responsibility for each of the 

agency’s data elements should be assigned to a single data steward 

(see chapters 1–3). These stewards should work with the security team to 

determine the sensitivity of, and appropriate level of security for, every data 

element. Together, they may 

• classify each item’s level of confidentiality (e.g., public or restricted); 

and 

• identify the user groups, by characteristics such as job function or 

“need to know,” that should be granted access to each element. 

As discussed in chapter 3, data stewards should also be the point of contact 

for requests of data they manage, and may authorize data sharing in 

response to those requests. 

The first step in securing an education agency’s data is developing a clear view of its 

information “landscape.” 

	 Determine what the agency has. Take an inventory of all of the data the agency 

collects and maintains. 

	 Locate all the data. Document where the data are stored, including servers, 

individual computers, filing cabinets, and other media such as CDs and storage 

devices. 

	 Document the “ownership” of each data element. Each data element should be 

the responsibility of a single data steward, who should be clearly identified. 

	 Determine the sensitivity of each data element the agency manages based on 

privacy requirements under state and federal laws. Document the risks associated 

with exposure of sensitive information and/or assign a risk level to each element, 

perhaps through a simple rating scale. 

	 Document who has access to what data, including internal staff, contractors, 

vendors, and external users. Note who can do what with the data (e.g., manipulate 

vs. view) and record all the ways these authorized users can access the data. 

	 Determine and document how long various users will be allowed to access data 

and ensure access is denied once it is no longer appropriate. For example, agencies 

70 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
must diligently end or adjust staff access to data when they change positions or 

leave the agency. Also, states or districts (or school policymakers) must determine 

if teachers will be allowed to access personally identifiable information on their 

former students (some agencies update access by enrollment annually, while 

others grant teachers broad access to their former students’ information for a year 

or more, even into postsecondary). 

	 Document data sources. From what source systems do your data originate? Who 

sends them? 

	 Document data recipients. These may include federal agency departments, 

postsecondary institutions, research organizations, and all others who receive the data. 

	 Document how data are transmitted. Data might be transmitted electronically, 

mailed on paper, etc.

 Role-based data access 

Users should have easy access to the data in an LDS, and standardized reports 

showing aggregate data and analysis results should be publicly available. 

Additionally, for ad-hoc querying and analysis, the general public may be given 

access to aggregate statistics and to non-identifiable individual student records. For 

personally identifiable information, users should be granted varying levels of access 

depending on their role, needs, and responsibilities. For instance, through an online 

application, users could gain access to permitted information by signing on with their 

individual username and password. A student’s record may be made available to that 

student, as well as to his or her parents or guardians, current teachers, counselors, 

school, district administrators, etc. However, the specific information shared may vary

depending on the user’s identity; for example, only parents might be allowed to see 

a student’s lunchroom account balance. Researchers with appropriate contracts and 

permission may also be granted access to some personally identifiable data. 

The Data Policy Committee or Data Governance Committee should document 

accessibility by user roles, as well as for what purposes the data can be used. These 

specifications should be granular enough to detail the rights of each type of user, 

allowing access to the information each is entitled to, but no more. Also, while 

access rights may be determined at the state level, some software programs allow 

delegation of access rights to occur at the local level. In other words, the application 

allows “delegated” district administrators to grant certain users access to sensitive 

information, if they deem such access appropriate. 

While some education agencies run multiple software systems to access 

separate databases or data sets, others implement “single sign-on” systems to 

manage user identity and streamline access. Rather than assigning each user a 

different password and username for each data system, these agencies maintain a 

single username and password for each user and grant appropriate access across 

applications. Each user's access rights can be tailored based on his or her 

specific need for the data in each system. 

Book Three of Four: Effectively Managing LDS Data 71 
Keep Only the Data Your Stakeholders Need 

The more data an agency has, the more it must secure. Although stakeholders may 

demand a wide range of information, each agency should consider disposing or securely 

archiving any data deemed unnecessary, especially if they contain personally identifiable 

information. In this stage of the life cycle of information (see chapter 1 of Book Two: 

Planning and Developing an LDS), data should be destroyed in a manner consistent with 

their sensitivity. 

	 Completely remove information (“wipe”) from old computers and storage devices 

before disposing of them; and shred, burn, or pulverize unnecessary paper 

records. Remote and onsite staff should follow the same procedures. 

	 Going forward, collect only the information required to meet business and 

stakeholder needs. 

	 Create a record-retention policy that details what information should be stored 

by the agency, how it should be secured, how long it should be kept, and how it 

should be destroyed once it is no longer needed. Agencies may set up a formal 

review process to assess data’s value and authorize disposal. 

Secure the Data 

Authentication is the verification of a user’s identity through 

means such as the submission of a unique password and/or other 

personal information. Authorization is the mechanism by which that 

authenticated user is granted access rights (e.g., the right to view 

data of varying degrees of sensitivity, or the right to manipulate data 

in addition to viewing them). 

Agencies need to stay on their guard, identifying vulnerabilities and adapting to everchanging security threats. Threats may come from within the organization or outside 

agency walls. The Internet, for instance, increases the risk to student privacy as individuals 

from the local education community or from across the globe can hack into data systems 

to change test scores, unleash viruses, or just wreak general havoc. 

	 Establish a group or office specifically focused on security issues, perhaps creating 

an enterprise-wide security plan; and implement security strategies to manage data 

access and use. 

	 Assign a security officer to lead this office. This staff member should be wellversed in all relevant privacy laws, and with the technology and business processes 

that facilitate compliance. The security officer may coordinate the agency’s 

security plan (authentication, intrusion detection, etc.) and must ensure that all 

staff are appropriately trained to protect the agency’s data. 

	 Store data in a secure location accessible only to authorized personnel, and lock 

when not in use. This would include all sensitive information contained on 

servers, computers, media such as CDs, or on paper. 

	 Automatically encrypt hard drives and use only password-protected thumb drives 

72 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Book Three of Four: Effectively Managing LDS Data 73

for transferring sensitive data (Houde et al. 2007).

	 Set access controls to the network and review them periodically. A user’s identity 

should be authenticated with a complex password, pass phrase, or other personal 

information. 

	 Based on the level of access determined by staff (e.g. data stewards) and 

implemented through technology, each particular user should only be given 

access to authorized information.

	 Use intrusion-detection systems to identify suspicious access to the network.

	 Establish and utilize infrastructure components such as firewalls, backups, and 

antivirus and antispyware software (EIMAC 2008).

	 Protect data while they are moving (“in motion”) between data systems or to data 

users. For instance, student-level data might be encrypted before they are fed from 

a source system into the LDS, or from the state agency’s LDS back to a district.

	 To help keep private data from getting into the wrong hands, convene a Data 

Request Review Board (see chapter 3) to establish a clear process for handling data 

requests in an orderly and consistent fashion. 

	 Create a contingency plan to facilitate a quick and appropriate response in 

the event data security is threatened or breached. This plan should specifically 

describe responses to a range of scenarios. For instance, how will the agency 

respond to network intrusion, a stolen laptop, or wrongful dissemination of 

sensitive information? Whom will you notify—law enforcement, staff, the 

individuals whose personal data have been compromised, the public, etc.—and 

should you use the phone, email, or other means of communication? How 

will damage to the system be controlled? How will the impact of the breach be 

assessed?

Even with a solid security plan, agency data will 

not be secure without proper implementation. All 

staff, not just IT, should understand the sensitivity 

of the records and the vulnerabilities of the system, 

and security should be a priority in everyone’s daily 

routine. Improving agency security therefore involves a certain degree of 

culture change. 
 Disaster preparedness 

Natural and manmade disasters can severely disrupt educational activities, displacing 

students and interrupting services including data collection, maintenance, and use 

of data. LDSs, which may consolidate a wide range of data and data processes, are likely 

to be necessary for carrying out day-to-day business (“mission critical”). Moreover, when 

disaster strikes, these systems are vital to mitigating the effects of the crisis. For instance, 

the LDS can be used for enrolling displaced students in the appropriate grades, courses, and 

programs; meeting accountability requirements; and efficiently allocating funding. Agencies 

should carefully plan for destructive or disruptive events, including physically safeguarding 

the system, designing the system architecture to facilitate displaced student tracking (e.g., 

including data elements such as displacement identifiers and event descriptors), and creating 

policies for tracking students and exchanging data after a crisis. 

The implementation of a state-level LDS offers several clear advantages in a crisis. For example, 

consolidating agency data into a single data store will facilitate preparations for a disaster, 

as the need to modify and coordinate a multitude of silo systems would be limited. During 

and after a crisis, the efficient exchange of high-quality, student-level data that are verified 

by centralized procedures and marked by individual identifiers via an LDS may help in many 

ways. For instance, administrators can use the data to efficiently and precisely target resources, 

provide displaced students with the proper services, and pass the scrutiny of federal data audits. 

Aggregate counts, on the other hand, may be inaccurate as they often contain duplicate counts. 

They may also be untimely as they take more time to produce, leading to slow and inefficient 

distribution of resources and a haphazard provision of services. Finally, states with an LDS may 

be able to ease the reporting burden of districts needing federal aid, as they may be able to deal 

directly with federal agencies on school districts’ behalf. 

(National Forum on Education Statistics 2010) 

Additional resources: 

Disaster preparedness 

	 Crisis Data Management: A Forum Guide to Collecting and Managing Data About 

Displaced Students (2010) 

http://nces.ed.gov/forum/pub_2010804.asp 

 This guide focuses on data issues surrounding the displacement of students. It offers best 

practices and lessons learned regarding planning activities that can help mitigate the 

disruptive effects of crises on education data systems and business continuity. 

 	 Disaster Prevention and Recovery for School System Technology (ESP Solutions 2005) 

Available at http://www.espsolutionsgroup.com/resources.php. 

This white paper offers best practices related to disaster recovery planning. 

 IT Disaster Recovery and Business Continuity Toolkit: Planning for the Next Disaster 

(NASCIO 2007) 

http://www.nascio.org/publications/documents/nascio-drtoolkit.pdf 

 This toolkit provides a framework to assist CIOs and other agency leaders in the 


development of IT disaster preparedness plans. It includes a collection of 


high-level checklists and questionnaires to help focus preparation efforts. 


74 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Book Three of Four: Effectively Managing LDS Data 

Train and Inform Data Handlers
 

The Forum has more detailed information... 

...about data ethics: 

• The Forum Code to Data Ethics (2010) 

http://nces.ed.gov/forum/pub_2010801.asp 

• The Forum Guide to Data Ethics Online Course (2010) 

http://nces.ed.gov/forum/dataethics_course.asp 

With an LDS, more staff members will gain access to sensitive student-level data. As access 

to data expands, so must security and confidentiality training to avoid unlawful data 

sharing or use. Some best practices follow. 

	 Establish a training plan that tailors instruction to staff with different levels of 

access to sensitive data, giving those with more access more rigorous training. 

Training may also be tailored to specific groups of data users based on job 

functions. 

	 Monitor the access granted to staff and provide additional training as needed. 

	 Train and retrain staff and contractors periodically on the proper and ethical 

handling of sensitive data. This training should be completed before access to 

sensitive data is granted. 

	 Hold staff accountable for failures to adhere to the agency’s security procedures 

and confidentiality policies. 

	 Require employees and contractors to sign nondisclosure agreements. 

	 Require researchers to sign memoranda of understanding (MOU) that detail 

privacy and security requirements. 

	 Establish a means for communicating security issues to staff, perhaps via a 

security website, email newsletters, or meeting updates from security staff; 

communications should be both periodic and impromptu as issues surface. 

	 Specify security requirements in requests for proposals (see chapter 14 of Book

Two: Planning and Developing an LDS) and assess prospective vendors’ security 


practices or software specifications; this will help ensure the service provider or 


product can, in fact, meet the agency’s security needs. 


75 
LDS Lore: 

Identity theft in the printer room
 

At the school district office, Margaret typed in her password and accessed the teacher information system. She 

found the data she needed and sent it to the printer. The 

phone rang—Sally was calling about lunch. Starving, Margaret 

grabbed her coat and headed out for a burrito. Meanwhile, at 

the printer, Bonnie lifted a stack of unclaimed paper from the 

tray and set it on the table. She waited for her job to collate, 

snatched it, and left the room. After lunch, Margaret was 

extremely busy as one meeting flowed into another until it was 

time to make the commute home. Before close of business, Chris, 

the district's data governance coordinator, was making his 

rounds and noticed the pile of papers on the table. He picked 

them up, quickly realized what they were, and raced back to 

his office. Meanwhile, Richard, the new janitor, was buttoning up his uniform. He'd heard that staff at the district office sometimes 

leave pretty valuable information lying around (the kind he'd 

read about in that non-disclosure agreement he'd signed) and 

he was looking forward to getting his hands on some. Later, at 

the office, he steered the vacuum around the desks, down the 

empty halls, and into the printer room, keeping his eyes peeled. 

He checked the recycling bins, garbage cans, table tops, looking 

everywhere for names, Social Security numbers, dates of birth— 

the good stuff that he could turn into cash. But even though he 

cleaned the place from wall to wall, nothing turned up. Better 

luck tomorrow night, he told himself before turning off the lights. The following morning, Sally opened her email to find a message from Chris calling a mandatory meeting for all data 

stewards. The subject: Safeguarding sensitive data. A second 

email came from the district's security officer announcing a new 

round of training on data security. Just then, it all came back 

to Sally: the print job...the Mexican food...the meetings. Sally 

thought, as she rushed to the meeting, "I hope I didn't cause 

any damage." 

76 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
 

appendix a 

overview of the lDS 

guide Series 

The Traveling Through Time: The Forum Guide to Longitudinal Data Systems series consists of 

four guides intended to help state and local education agencies meet the many challenges 

involved in developing a robust longitudinal data system (LDS), populating them with 

quality data, and using this new information to improve the education system. 

Book One of Four: What is an LDS? focuses on the fundamental questions of what an 

LDS is (and what it is not), what steps should be taken to achieve a sound and successful 

system, what components make up an ideal system, and why such a system is of value 

to education. Book Two of Four: Planning and Developing an LDS, discusses the critical 

planning and development phases of an LDS project, from stakeholder engagement and 

needs assessment all the way through to system evaluation. Book Three of Four: Effectively 

Managing LDS Data explores several fundamental challenges of data management, 

focusing on data governance, data quality, privacy, and security. Finally, Book Four of Four: 

Advanced LDS Usage, will address the effective utilization of LDS data, discussing the 

users and uses of the data; and emphasize the need for effective training and professional 

development. The figure on the next page lays out the major issues covered in each of the 

four books in this Forum guide series. 

Book Three of Four: Effectively Managing LDS Data 77 
78 Traveling Through Time: The Forum Guide to Longitudinal Data Systems

Book I: What Is an LDS?

 Understanding what an LDS is (and is not)

 Appreciating the organizational steps needed to 

institute and effectively use an LDS

 Identifying the technical features and 

capabilities of an effective LDS and the 

additional features that can enhance the 

system’s utility

 Recognizing the benefits of an LDS

Book II: Planning and 

Developing an LDS

 Engaging stakeholders

 Describing the current system 

 Envisioning the desired system

 Defining needs, including data and functionality

 Gaining buy-in and funding

 Building relationships

 Writing an RFP 

 Building or buying a system or components

 Transferring knowledge (e.g., from developers 

to staff)

 Defining and measuring success

 Refining the system

Book III: Effectively 

Managing LDS Data

 Defining governance structure

 Defining roles and responsibilities

 Collaborating to improve data quality and 

streamline operations

 Managing changes to the system

 Training staff to ensure data quality

 Auditing/validating data at all levels 

 Establishing/following data standards

 Securing data to protect privacy 

 Providing users access to key data

Book IV: Advanced 

LDS Usage

 Collecting, storing, and delivering key data 

 Developing useful reports to fulfill common 

data requests and needs 

 Developing user-friendly data tools to facilitate 

access and analysis

 Training users to utilize the technology

 Building awareness, understanding, and 

analytical capacity

Book One: What is an LDS? is available online at: http://nces.ed.gov/forum/pub_ 2010805.asp

Book Two: Planning and Developing an LDS is available online at: 

http://nces.ed.gov/forum/pub_ 2011804.asp

To order a print copy, please contact: 

ED Pubs

U.S. Department of Education

P.O. Box 22207

Alexandria, VA 22304

Or call toll free 1–877–4ED–PUBS or order online at http://www.edpubs.gov.
appendix B 

references 


Aarons, D. (2009). Leading the Charge for Real-Time Data. Education Week, June 3, 2009. 

Available at http://www.edweek.org/ew/articles/2009/06/03/33dataleader_ep.html?tkn=PSN 

FXRv4MZsj0rDA8PJJQE7WcHpl%2F4ehVf78. 

Chatis Consulting. Data Management Committee: Critical Data Issues. (Unpublished.) 

Data Quality Campaign (DQC 2007). Maximizing the Power of Education Data While 

Ensuring Compliance with Federal Student Privacy Laws: A Guide for Policymakers. 

Washington, DC: Data Quality Campaign (DQC). 

Data Quality Campaign (DQC 2008). Data Governance: Changing Culture, Breaking Down 

Silos, and Deciding Who Is in Control. Washington, DC: Data Quality Campaign (DQC). 

Data Quality Campaign (DQC 2009). The Next Step: Using Longitudinal Data Systems To 

Improve Student Success. Washington, DC: Data Quality Campaign (DQC). 

Education Counsel (2008). Needed Changes in FERPA Law, Regulations, or Guidance to 

Harmonize FERPA with the Functions of State Longitudinal Data Systems. (Draft.) Available at 

http://www.dataqualitycampaign.org/files/publication-needed_ changes_ ferpa_ not_ addressed_ 

regulations-122808.pdf. 

Education Information Management Advisory Consortium (EIMAC 2008). The Need for 

Data Governance. Retrieved from http://www.ccsso.org/content/pdfs/EIMAC%20Brief%20 

4%200308.pdf on September 24, 2008. 

ESP Solutions (2008). Confidentiality and Reliability Rules for Reporting Education Data: A 

Guide for Establishing Decision Rules for Disaggregating and Reporting Assessment Results, and 

Other Indicators. Austin, Texas: ESP Solutions Group. 

Federal Trade Commission. Protecting Personal Information: A Guide for Business. Retrieved 

from http://www.ftc.gov/bcp/edu/multimedia/interactive/infosecurity/index.html on January 

11, 2010. 

Hill, E. (2008). Redefining Student Data Access Policy. Sacramento, California: Legislative 

Analyst’s Office. 

Houde, D.; Vocca, M.; Williams, D.; and Canada, B. (2007). Security, Data Access. 

Presentation given at November 2007 IES SLDS Grantee Meeting in Arlington, Virginia. 

Available at http://nces.ed.gov/programs/slds/nov07_presentations.asp#sessionvi. 

Houde, D. (2008). Arizona Department of Education Information Security Update. Presented at the 

annual summer meeting of the National Forum on Education Statistics in Bethesda, Maryland. 

Book Three of Four: Effectively Managing LDS Data 79 
 

Kansas State Department of Education (KSDE 2007). Data Governance: The Kansas 

Approach. Presentation given at the Education Information Management Advisory 

Consortium (EIMAC) Spring Meeting in Washington, DC. 

Kansas State Department of Education (KSDE 2008). Data Governance Program Version 2.2. 

(Unpublished.) 

National Forum on Education Statistics (2010). Crisis Data Management: A Forum Guide to 

Collecting and Managing Data About Displaced Students (NFES 2010–804). U.S. Department 

of Education. Washington, DC: National Center for Education Statistics. 

National Postsecondary Education Cooperative (1998). Unit Record Versus Aggregate Data: 

Perspectives on Postsecondary Education Data Collection, Retention, and Release (NCES 98–280). 

U.S. Department of Education. Washington, DC: National Center for Education Statistics. 

NASCIO (2008). Data Governance—Managing Information as an Enterprise Asset: Part I—An 

Introduction. Lexington, Kentucky: NASCIO. 

Nunn, J. and Harper Mainzer, K. (2006). Longitudinal Data Systems: Summary of Current 

Issues and Potential Uses. Retrieved from http://nces.ed.gov/programs/slds/ldsshare/slds.aspx on 

December 4, 2008. 

Redman, T. (2001). Data Quality: The Field Guide. Boston, Massachusetts: Digital Press. 

Rozelle, R. (2006). The Tennessee Statewide Longitudinal education Data System (SLeDS): 

The Governance Process. Presentation given at the 2006 IES SLDS Grantee Meeting in 

Washington, DC. 

Rozelle, R. (2008). Role Descriptions and Responsibilities in the Realm of Data Governance. 

Center for Educational Leadership and Technology. Available at 

http://www.dataqualitycampaign.org/files/publication-role_ descriptions_and_responsibilities_ 

in_ the_realm_ of_ data_ governance-022608.pdf. 

Schutte, S.; Edwards, S.; Fadaoff, S.; Ogle, T.; and Ligon, G. (2009). The Process of Data 

Quality: State Discussion with Alaska, Wyoming, Missouri, and California. Presentation given 

at the 22nd Annual MIS Conference in Seattle, Washington. As cited in The Process for 

Ensuring Data Quality, ESP Solutions (2009). 

Tennessee Department of Education (draft 2008). Data Manager Manual, Version 1.0. 

Retrieved from http://www.dataqualitycampaign.org/files/publication-data_manager_manual. 

pdf on December 12, 2008. 

U.S. Department of Education (2008). Family Educational Rights and Privacy; Final 

Rule. Federal Register, 73 (237). Available at http://www2.ed.gov/legislation/FedRegister/ 

finrule/2008-4/ 120908a.pdf. 

Viadero, D. (2006). Scholars Cite Privacy Laws as Obstacle. Education Week, January 18, 2006. 

Zhao, X.; O’Connor, B.; and Barroso, G. (2006). Objective of the Data Security Model. 

InfoManagement Direct, August 2006. Available at 

http://www.information-management.com/infodirect/20060825/1061606-1.html. 

80 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
appendix C 

additional resources
 

Chapter 1. Data Governance 

Data Governance: Changing Culture, Breaking Down Silos, and 

Deciding Who is in Control 

Data Quality Campaign (DQC 2008). 

http://dataqualitycampaign.org/files/meetings-dqc_quarterly_issue_brief-072908.pdf 

This issue brief discusses the benefits of data governance, conditions helpful in successfully 

establishing data governance, and more. It also includes case studies of three states’ 

experiences. 

The Need for Data Governance 

Education Information Management Advisory Consortium (EIMAC 2008). 

Retrieved from http://www.ccsso.org/content/pdfs/EIMAC%20Brief%204%200308.pdf on 

September 24, 2008. 

Data governance is driven by the need for data quality. This three-page brief discusses the 

components of good data governance, including leadership, data quality management, 

controlled analysis and reporting, security and confidentiality, resource management, and 

data use and accessibility. 

The Next Step: Using Longitudinal Data Systems to Improve 

Student Success 

Data Quality Campaign (DQC 2009). 

http://dataqualitycampaign.org/files/nextstep.pdf 

See page 10 for a section on data governance. 

DataFlux Knowledge Center 

http://www.dataflux.com/getdoc/3a87235c-d2b3-4c24-a794-6198669754da/data-governance.aspx 

This page contains a host of white papers and other resources related to data governance. 

See the Data Governance Maturity Model for a good summary of the issue. 

NASCIO Enterprise Architecture and Governance Committee 

http://www.nascio.org/committees/ea 

The Enterprise Architecture program of the National Association of State Chief 

Information Officers (NASCIO) was developed to enable the mission of state and local 

government. This web page contains a number of helpful resources on data governance, 

including a three-part series of papers on the topic. 

Book Three of Four: Effectively Managing LDS Data 81 
 

The Data Governance Institute 

http://www.datagovernance.com 

This website contains an extensive collection of resources on data governance, including 

basic information, case studies, frameworks, books, and more. 

The Data Administration Newsletter 

http://www.tdan.com 

This newsletter publishes many useful articles on data governance. 

Management of an Education Information System 

ESP Solutions (2005). 

http://www.espsolutionsgroup.com/espweb/assets/files/ESP_ EDInfo_ System_ Mgmt_ORG.pdf 

This document describes how education agencies can organize resources for effective 

management of the information they acquire, store, process, and report. 

Harnessing the Potential for Research of Existing Student 

Records Databases: An Action Agenda 

National Center for Higher Education Management Systems (2005). 

http://dataqualitycampaign.org/resources/31 

This brief resulted from a meeting of academic researchers responsible for several state 

student unit record systems. Pages 6–13 discuss issues related to data governance. 

Chapter 2: Basic Steps to Establishing Data Governance 

Implementing Data Governance as the Foundation of a LDS 

Presentation 

Tennessee Department of Education (2008). 

http://dataqualitycampaign.org/resources/96 

This presentation summarizes Tennessee’s approach to implementing data governance, 

including roles, helpful strategies, and lessons learned. 

Role Descriptions and Responsibilities in the Realm of Data 

Governance: Managing Information Assets 

Tennessee Department of Education (2008). 

http://dataqualitycampaign.org/resources/97 

This document describes eight data management jobs and roles. It also includes a matrix of 

data governance activities, and the people/groups that need to be involved in each activity. 

LDS Share 

The following documents are available at http://nces.ed.gov/programs/slds/ldsshare/slds.aspx: 

Data Management Committee Critical Data Issues Log 

Tennessee Department of Education (2007). 

This is a template for tracking critical data issues, which may be tracked by a Data 

Governance Committee. 

82 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
 

 

Policies and Procedures Review: Data Management 

Tennessee Department of Education (2006). 

This document from Tennessee establishes policy and procedures governing 

data management and quality for the Department of Education. Roles and 

responsibilities of the data governance structure are also outlined. 

Chapter 3: Groups, Roles, and Responsibilities of Data 

Governance 

Role Descriptions and Responsibilities in the Realm of Data 

Governance: Managing Information Assets 

Tennessee Department of Education (2008). 

http://dataqualitycampaign.org/resources/97 

This document describes eight data management jobs and roles. Also included is a matrix 

of data governance activities and the people/groups that need to be involved in each 

activity. 

Data Governance Program, Version 2.2 

Kansas State Department of Education (2008). 

http://www.dataqualitycampaign.org/files/KSDE_ Data_Governance_ Program_Ver_ 2.2.pdf 

This document details Kansas’ approach to data governance. It includes an overview of the 

issue, and handbooks for the two major committees involved and for data stewards. 

The DGI Data Governance Framework 

Data Governance Institute. 

http://www.datagovernance.com/fw_the_ DGI_data_governance_ framework.html 

This resource presents a framework to help organizations classify, organize, and 

communicate complex activities related to data-based decisionmaking. 

Data Governance: Changing Culture, Breaking Down Silos, and 

Deciding Who is in Control 

Data Quality Campaign (DQC 2008). 

http://dataqualitycampaign.org/files/meetings-dqc_quarterly_issue_brief-072908.pdf 

This issue brief discusses the benefits of data governance, conditions helpful in successfully 

establishing data governance, and more. It also includes case studies of three states’ 

experiences. 

Governance Structure, LDS Project Team Organization, and 

Sustainability 

http://nces.ed.gov/programs/slds/zip/granteemeeting06_5c.zip 

In this presentation, several states report on the governance structures that have helped 

them succeed in implementing changes required in developing a longitudinal data system. 

Book Three of Four: Effectively Managing LDS Data 83 
 

 

LDS Share 

The following documents are available at http://nces.ed.gov/programs/slds/ldsshare/slds.aspx: 

Effective Governance Models for Managing Data Systems 

Tennessee Department of Education (2008). 

This presentation details Tennessee’s strategies and recommendations for 

implementing data governance. 

Data Managers Working Group Charter 

Michigan Department of Education (2007). 

This document outlines the key roles in Michigan’s Data Managers Working 

Group, which is comparable to the Data Governance Committee in this guide. Also 

included are the group’s mission and its members’ responsibilities. 

Chapters 4–5: Data Quality 

Forum Guide to Building a Culture of Quality Data: 

A School and District Resource 

National Forum on Education Statistics (2004). 

http://nces.ed.gov/forum/pub_ 2005801.asp 

This guide was developed to help schools and school districts improve the quality of data 

they collect, and to provide processes for developing a “culture” of quality data by focusing 

on data entry—getting things right at the source. This guide shows how quality data can be 

achieved through the collaborative efforts of all staff. 

Forum Curriculum for Improving Education Data: 

A Resource for Local Education Agencies 

National Forum on Education Statistics (2007). 

http://nces.ed.gov/forum/pub_ 2007808.asp 

This curriculum supports efforts to improve the quality of education data by serving as 

training materials for K–12 school and district staff. It provides lesson plans, instructional 

handouts, and related resources; and presents concepts necessary to help schools develop a 

culture for improving data quality. 

Texas Education Agency Data Standards 

http://ritter.tea.state.tx.us/peims/standards/0910/index.html 

This web page presents up-to-date documentation on the Texas data standards and 

requirements. Section 1 includes a discussion of data-submission training. 

Texas Data Validation Monitoring 2008–2009 

http://ritter.tea.state.tx.us/pmi/datamon/2009/index.html 

This web page includes resources related to the Texas Education Agency’s data validation 

system, which analyzes data submitted by districts and charters and automatically identifies 

potential data anomalies in their reporting on leavers and dropouts, student assessments, 

and discipline. 

84 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Kansas Data Quality Certification Program 

Kansas State Department of Education. 

http://www.ksde.org/default.aspx?tabid=3377 

This website includes descriptions of the tracks for various roles (e.g., data entry personnel, 

administrators, etc.), many helpful resources, and more. Also see a presentation about the 

program, Yellow Brick by Yellow Brick: Using a Professional Development Program to Strengthen Data 

Quality in Kansas, at http://nces.ed.gov/whatsnew/conferences/mis/2009/presentations/v_d.zip. 

Nebraska’s Data Validation Process 

Beecham and Tagart (2008). 

http://nces.ed.gov/programs/slds/zip/08_ f_10a.zip 

This presentation, and the supporting document, outlines Nebraska’s approach to data 

validation, which flags errors and presents data issues through reports on a versatile 

website. There, authorized staff members can review potential errors, make corrections, and 

approve the information. 

The Process for Ensuring Data Quality 

ESP Solutions (2009). 

http://www.espsolutionsgroup.com/espweb/assets/files/ESP_ Process_ for_ Ensuring_ Data_ 

Quality_ORB%282%29.pdf 

This two-part series includes best practices and principles to help education agencies 

achieve better data quality. 

Process Illustration: Steps for Ensuring Data Quality 

ESP Solutions (2004). 

http://www.espsolutionsgroup.com/espweb/assets/files/ESP_ Data_ Quality_ Illustration.pdf 

This illustration details key questions and steps involved in ensuring data quality. 

The Data Quality Imperative, Data Quality Series—Part I 

ESP Solutions (2007). 

http://www.espsolutionsgroup.com/espweb/assets/files/ESP_ Data_ Quality_ Imperative_ORG.pdf 

This is the first part of a series that merges foundations of data quality from the formal 

literature on information systems with practical lessons on ensuring data quality in the 

public education system. 

The Data Quality Manual, Data Quality Series—Part II 

ESP Solutions (2007). 

http://www.espsolutionsgroup.com/espweb/assets/files/ESP_ Data_ Quality_ Manual_ PartII_ORG.pdf 

This is the second part of a series on data quality. It focuses on practical lessons learned on 

ensuring data quality gained from real school experiences. 

Book Three of Four: Effectively Managing LDS Data 85 
 

 

LDS Share 

The following documents are available at http://nces.ed.gov/programs/slds/ldsshare/slds.aspx: 

Policies and Procedures: Verification of Electronic Data 

Systems and Accompanying Letter 

State of Tennessee Department of Education (2006). 

This document establishes policies and procedures for governing data system 

verification of quality. The accompanying letter is also available. 

Statistical Process Control 

Maryland State Department of Education (2007). 

This document describes Maryland’s statistical process control, a validation process 

added to the state’s education data collection system to help avoid errors published 

online for state and federal compliance reporting. 

Chapter 6: Data Standards 

NCES Handbooks Online 

National Center for Education Statistics. 

http://nces.ed.gov/programs/handbook 

The NCES Data Handbooks provide guidance on consistency in data definitions and 

maintenance for education data, so that such data can be accurately aggregated and 

analyzed. The online database provides the nonfiscal handbooks in a searchable web tool 

and includes data elements for students, staff, and education institutions. 

National Education Data Model 

http://nces.ed.gov/forum/datamodel 

The National Education Data Model catalogs the data used in P–12 education and 

describes the relationships among those data. It is designed to be used as a reference tool 

to facilitate the identification, merging, and matching of data across different systems; to 

provide similar descriptions across local education agency (LEA) systems, across LEAs, and 

from LEAs to the state and federal government; and to specify the content and structure of 

logical and physical data models. 

Common Education Data Standards Initiative 

http://commondatastandards.org 

This initiative is a national, collaborative effort to develop voluntary, common data 

standards for a key subset of K–12 (e.g., demographics, program participation, course 

information) and to-postsecondary education transition variables. The initiative aims to 

identify a list of key K–12 and to-postsecondary transition variables; and agree on standard 

definitions, code sets, business rules, and technical specifications for these variables. 

Expansion into PK and the workforce will be considered in the future. 

86 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
DQC Standards, Interoperability, and Portability Thought 

Leaders Meeting 

Data Quality Campaign (DQC 2009). 

http://www.dataqualitycampaign.org/events/247 

This webpage includes a number of presentations from a 2009 policymakers meeting in 

Washington, DC, as well as resources related to data standards. 

SIF Association Implementation Toolkit 

Schools Interoperability Framework (SIF) Association. 

http://www.sifinfo.org/us/tool_kit.asp 

This collection of documents is intended to help education institutions in the SIF 

implementation process. It includes planning questions (scope, desired automation, data 

needs, expected changes), RFP language, an implementation planning toolkit, and support 

resources. 

Financial Accounting for Local and State School Systems: 

2003 Edition 

National Center for Education Statistics (2004). 

http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2004318 

This NCES handbook was designed to be used as the national standard for state 

education departments (SEA) reporting financial data, and for school districts preparing 

comprehensive annual financial reports (CAFR). 

The Right Data to the Right People at the Right Time: 

How Interoperability Helps America’s Students Succeed 

Data Quality Campaign (DQC 2007). 

http://dataqualitycampaign.org/files/Meetings-DQC_ Quarterly_ Issue_ Brief_061307.pdf 

This paper reviews the needs for, benefits of, and concurrent efforts to establish 

interoperable education systems. It offers several key definitions, a case study section, and a 

list of interoperability examples from other industries. 

Secondary School Course Classification System: School Codes 

for the Exchange of Data (SCED) 

National Center for Education Statistics (2007). 

http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2007341 

This NCES handbook provides a taxonomy for assigning standard codes to secondary 

school courses in 22 major subject areas. It also includes a content description for each 

course, and instructions on how to use the taxonomy in coding courses. 

Articulating the Case for Course Numbers 

ESP Solutions (2006). 

http://www.espsolutionsgroup.com/espweb/assets/files/ESP_Course_ Numbers_ORG.pdf 

This booklet argues for the use of common course codes nationwide. 

Book Three of Four: Effectively Managing LDS Data 87 
 

 

The Open Group 

http://www.opengroup.org 

The Open Group is a consortium of public and private sector organizations that promote 

open standards and global interoperability in order to enable access to integrated 

information both within and between enterprises. The organization provides a SIF 

certification program for vendors. 

Metadata and data dictionaries 

Forum Guide to Metadata: The Meaning Behind Education Data 

National Forum on Education Statistics (2009). 

http://nces.ed.gov/forum/pub_ 2009805.asp 

This guide empowers more effective use of data as information. To accomplish this, the 

publication explains what metadata are, why they are critical to sound education data 

systems, what components comprise a metadata system, what value metadata bring to data 

management and use, and how to implement and use a metadata system. 

Minnesota Department of Education Data Dictionary 

Minnesota Department of Education. 

http://education.state.mn.us/mde-dd 

Minnesota has prepared and published its education data dictionary. This version has the 

same content and form as the state’s internal data dictionary, except that data elements 

that might be sensitive from a security point of view are not revealed. 

South Carolina LDS Project: Data Dictionary–Data Model 

South Carolina Department of Education (2006). 

Available via LDS Share at http://nces.ed.gov/programs/slds/ldsshare/slds.aspx 

This document illustrates South Carolina’s longitudinal data system project’s data 

dictionary/data model. 

LDS Share 

The following documents are available at http://nces.ed.gov/programs/slds/ldsshare/slds.aspx: 

Strategic Approach For Developing the Longitudinal Data 

System Data Dictionary 

Wisconsin Department of Public Instruction (2007).
 

This working document outlines the approach for developing a data dictionary for 


the Wisconsin Department of Public Instruction’s longitudinal data system.
 

Agency-Wide Data Dictionary Planning Project 

Wisconsin Department of Public Instruction (2007).
 

This document details the planning and the implementation projects related to the 


Wisconsin Department of Public Instruction’s agency-wide data dictionary.
 

88 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Chapter 7: Privacy and Confidentiality 

Basic Concepts and Definitions for Privacy and Confidentiality 

in Student Education Records 

National Center for Education Statistics (2010). 

http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2011601 

This technical brief discusses basic concepts and definitions that establish a common set 

of terms related to the protection of personally identifiable information, especially in 

education records in the Statewide Longitudinal Data Systems (SLDS). This Brief also 

outlines a privacy framework that is tied to Fair Information Practice Principles that have 

been promulgated in both the United States and international privacy work. 

Statistical Methods for Protecting Personally Identifiable 

Information in Aggregate Reporting 

National Center for Education Statistics (2010). 

http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2011603 

This technical brief examines what protecting student privacy means in a reporting context. 

To protect a student’s privacy, the student’s personally identifiable information must 

be protected from public release. When schools, districts, or states publish reports on 

students’ educational progress, they typically release aggregated data—data for groups of 

students—to prevent disclosure of information about an individual. However, even with 

aggregation, unintended disclosures of personally identifiable information may occur. 

Current reporting practices are described and each is accompanied by an example table 

that is used to consider whether the intended protections are successful. 

Data Stewardship: Managing Personally Identifiable Information 

in Student Education Records 

National Center for Education Statistics (2010). 

http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2011602 

This technical brief focuses on data stewardship, which involves each organization’s 

commitment to ensuring that privacy, confidentiality, security, and the appropriate use of 

data are respected when personally identifiable information is collected. Data stewardship 

involves all aspects of data collection, from planning, collection and maintenance to use 

and dissemination. The Brief also discusses internal control procedures that should be 

implemented to protect personally identifiable information, including the use of unique 

student identifiers and linking codes, workforce security, authorization for access, role 

based access to student record data, permitted uses, and the handling of data breaches. 

This Brief concludes with a discussion of accountability and auditing, including an 

overview of the types of audit activities that can be implemented to ensure that all stages 

of data stewardship have been successfully implemented. 

Forum Guide to the Privacy of Student Information: 

A Resource for Schools 

National Forum on Education Statistics (2006). 

http://nces.ed.gov/forum/pub_ 2006805.asp 

This guide helps school and local education agency staff to better understand and apply 

FERPA, a federal law that protects privacy interests of parents and students in education 

Book Three of Four: Effectively Managing LDS Data 89 
records. (Note: The Forum is currently developing a single revised guide that will replace 

all current privacy resources about schools, LEAs and SEAs.) 

Forum Guide to Protecting the Privacy of Student Information: 

State and Local Education Agencies 

National Forum on Education Statistics (2004). 

http://nces.ed.gov/forum/pub_ 2004330.asp 

This guide presents a general overview of privacy laws and professional practices that 

apply to information collected for, and maintained in, student records. The document 

also provides an overview of key principles and concepts governing student privacy; 

summarizes Federal privacy laws including recent changes; identifies issues concerning 

the release of information to both parents and external organizations; and suggests good 

data management practices for schools, districts, and state education agencies. (Note: The 

Forum is currently developing a single revised guide that will replace all current privacy 

resources about schools, LEAs and SEAs.) 

Privacy Issues in Education Staff Records 

National Forum on Education Statistics (2000). 

http://nces.ed.gov/forum/pub_ 2000363.asp 

This publication addresses key concepts in protecting and managing information on staff 

records. It does not provide legal guidelines but, rather, addresses the federal Freedom of 

Information and Privacy Acts, and offers principles of best practice. 

Redefining Student Data Access Policy 

Legislative Analyst’s Office (2008). 

http://www.lao.ca.gov/2008/edu/student_data_access/student_data_access.pdf 

This brief proposes policy changes to the California legislature that would ease limitations 

on instructors’ and policymakers’ use of student data to improve instruction while still 

protecting student privacy. 

Protecting Student Records and Facilitating Education 

Research: A Workshop Summary 

National Research Council (2008). 

http://www.nap.edu/catalog.php?record_id=12514#toc 

To explore possibilities for data access and confidentiality in compliance with FERPA, the 

National Academies and the American Educational Research Association convened the 

Workshop on Protecting Student Records and Facilitating Education Research in April 

2008. This document contains a wealth of information on a broad range of related subjects. 

Confidentiality and Reliability Rules for Reporting 

Education Data 

ESP Solutions (2008). 

http://www.espsolutionsgroup.com/espweb/assets/files/ESP_Confidentiality_ Reliability_ORG.pdf 

This extensive report fleshes out the confidentiality and reliability issues that states encounter 

when reporting education data. It also offers criteria for agencies to use when establishing 

the rules for, and selecting, a minimum number of students to report as subgroups in data 

sets; and performance reports such as adequate yearly progress (AYP). In particular, see pages 

26–32 for a discussion on methods of manipulating data to protect confidentiality. 

90 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Family Educational Rights and Privacy Act (FERPA) 

Family Policy and Compliance Office (FPCO) 

http://www2.ed.gov/policy/gen/guid/fpco/index.html 

This office of the U.S. Department of Education administers FERPA, and its website 

contains a host of resources on the law including current regulations, legislative history, 

and guidance on disclosures. 

Final FERPA Regulations 

U.S. Department of Education (2008). 

http://www2.ed.gov/legislation/FedRegister/finrule/2008-4/120908a.pdf 

These regulations were issued by the U.S. Department of Education and have been 

effective since January 2009. This revised interpretation of the law contains extensive 

discussions of public comments received about the previous version of the regulations, and 

all changes are noted. 

FERPA Final Rule: Section-by-Section Analysis 

Family Policy and Compliance Office (FPCO 2008). 

http://www2.ed.gov/policy/gen/guid/fpco/pdf/ht12-17-08-att.pdf 

This document highlights the changes in the U.S. Department of Education’s new 

interpretation of FERPA. 

Forum Guide to the Privacy of Student Information: 

A Resource for Schools 

National Forum on Education Statistics (2006). 

http://nces.ed.gov/forum/pub_ 2006805.asp 

This guide helps school and local education agency staff to better understand and apply 

FERPA, a federal law that protects privacy interests of parents and students in education 

records. (Note: The Forum is currently developing a single revised guide that will replace 

all current privacy resources about schools, LEAs and SEAs.) 

Forum Guide to Protecting the Privacy of Student Information: 

State and Local Education Agencies 

National Forum on Education Statistics (2004). 

http://nces.ed.gov/forum/pub_ 2004330.asp 

This guide presents a general overview of privacy laws and professional practices that 

apply to information collected for, and maintained in, student records. The document 

also provides an overview of key principles and concepts governing student privacy; 

summarizes Federal privacy laws including recent changes; identifies issues concerning 

the release of information to both parents and external organizations; and suggests good 

data management practices for schools, districts, and state education agencies. (Note: The 

Forum is currently developing a single revised guide that will replace all current privacy 

resources about schools, LEAs and SEAs.) 

Book Three of Four: Effectively Managing LDS Data 91 
FERPA Resource Page 

Data Quality Campaign. 

http://dataqualitycampaign.org/resources/topics/13 

This page contains a wealth of resources on FERPA, including policy briefs, legal analyses, 

state memoranda of agreement and understanding, and more. 

FERPA Final Regulations and Analysis 2008 Resource Page 

Data Quality Campaign (DQC). 

http://dataqualitycampaign.org/resources/topics/14 

This page contains several helpful resources on the 2008 FERPA amendments, including 

analysis; suggestions for further revisions; and FERPA Mythbusters, a document that 

addresses some misconceptions about the privacy law. 

Maximizing the Power of Education Data While Ensuring 

Compliance with Federal Student Privacy Laws: A Guide for 

State Policymakers 

Data Quality Campaign (DQC 2007). 

http://dataqualitycampaign.org/files/Publications-FERPA_ A_Guide_ for_State_ Policymakers.PDF 

This issue brief analyzes how the new roles of state education agencies (and their 

longitudinal data systems) in data collection and sharing can be aligned with FERPA. 

Though this document is based on a particular interpretation of FERPA, it may serve as a 

guide to states as they build and use longitudinal data systems in ways that comply with 

FERPA and fully protect the privacy rights of students and parents. 

Health Insurance Portability and Accountability Act (HIPAA) 

Department of Health and Human Services (HHS) Health 

Information Privacy 

http://www.hhs.gov/ocr/privacy/index.html 

This website provides fact sheets, educational materials, and frequently asked questions 

about HIPAA. 

Joint Guidance on the Application of FERPA and HIPAA To 

Student Health Records 

Family Policy Compliance Office (FPCO 2008). 

http://www2.ed.gov/policy/gen/guid/fpco/doc/ferpa-hippa-guidance.pdf 

The document explains the relationship between FERPA and the HIPAA Privacy Rule, and 

addresses any confusion on the part of school administrators, health care professionals, and 

others as to how these two laws apply to student records. 

Issue Brief on Privacy Standards for Student Health Records 

National Association of School Nurses (2004). 

http://www.nasn.org/default.aspx?tabid=277 

Geared towards school health care providers, this resource discusses the relationship 

between FERPA and HIPAA and the laws’ implications on their work. The association also 

has a resource page on FERPA and HIPAA: http://www.nasn.org/default.aspx?tabid=146. 

92 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Special Education Technical Assistance Paper 

Washington State Office of Public Instruction (2003). 

http://www.k12.wa.us/SpecialEd/pubdocs/tap4.pdf 

This brief paper discusses the instances under which educational institutions are subject to 

HIPAA. 

Forum Curriculum for Improving Education Data: 

A Resource for Local Education Agencies 

National Forum on Education Statistics (2007). 

http://nces.ed.gov/forum/pub_ 2007808.asp 

Page 41 of this resource discusses privacy requirements for health records, including 

HIPAA and its interaction with FERPA. 

Individuals with Disabilities Education Act (IDEA) 

Forum Guide to Protecting the Privacy of Student Information: 

State and Local Education Agencies 

National Forum on Education Statistics (2004). 

http://nces.ed.gov/forum/pub_ 2004330.asp 

See pages 17–18 of this guide for a discussion of privacy protection under IDEA, and the 

law’s relationship to FERPA. (Note: The Forum is currently developing a single revised 

privacy product that will replace all current privacy resources about schools, LEAs, and SEAs.) 

Building the Legacy: IDEA 2004 

U.S. Department of Education. 

http://idea.ed.gov/explore/home 

This site offers a large collection of resources related to Part B of IDEA, which deals with 

children aged 3–21. 

Office of Special Education and Rehabilitative Services 

U.S. Department of Education. 

http://www2.ed.gov/about/offices/list/osers/osep/policy.html 

This site offers a host of resources related to IDEA legislation, regulations, and policy 

documents intended to help states implement the law. 

Joint Guidance on the Application of FERPA and HIPAA 

to Student Records 

Family Policy Compliance Office (FPCO 2008). 

http://www2.ed.gov/policy/gen/guid/fpco/doc/ferpa-hippa-guidance.pdf 

While this document focuses mainly on FERPA and HIPAA, a brief discussion of IDEA is 

offered in section II. 

Book Three of Four: Effectively Managing LDS Data 93 
 

National School Lunch Act (NSLA) 

National School Lunch Program 

U.S. Department of Agriculture, Food, and Nutrition Service. 

http://www.fns.usda.gov/cnd/lunch 

This site contains many resources on NSLA, including a fact sheet and regulations. 

Confidentiality of Free and Reduced Price Meal Eligibility 

Arkansas Department of Education (2009). 

http://arkedu.state.ar.us/commemos/static/fy0809/4093.html 

This memo discusses privacy requirements under NSLA, and how they relate to FERPA. 

Chapter 8: Data Security 

Enterprise Data Security 

Birrittieri, T. (2008). InfoManagement Direct, November 2008. 

http://www.information-management.com/infodirect/2008_99/10002235-1.html 

This article offers a simple “ownership” model of data security, focusing specifically on 

identifying data sensitivity and appropriately granting access to data user groups. It argues 

that group delineations should be based on users’ job functions rather than the data’s level 

of confidentiality. 

Forum Unified Education Technology Suite 

National Forum on Education Statistics (2005). 

http://nces.ed.gov/forum/pub_tech_suite.asp 

Part 5 of this resource addresses security, including tips on risk assessment and policy 

development. 

Objective of the Data Security Model 

Zhao, X.; O’Connor, B.; and Barraso, G. (2006). InfoManagement Direct, August 2006. 

http://www.information-management.com/infodirect/20060825/1061606-1.html 

This article provides a technical discussion of the data security “ownership” model. It 

describes a suggested set of processes including data classification, the identification of the 

risks associated with business functions and data systems, the creation and implementation 

of an access control policy, and the establishment of training and contingency plans. 

Data Environment Security 

Arizona Department of Education (2008). 

http://nces.ed.gov/forum/zip/sum08_techsecurity1.zip 

This presentation provides an overview of Arizona’s efforts to improve security and 

manage data access. It discusses the establishment of an office to focus on security 

challenges, as well as creating processes for managing and monitoring data usage, training 

staff, and developing a means for communicating security issues. The presentation also 

provides an overview of some of the technical solutions the agency has employed. 

94 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
 

 

 

Restricted-Use Data Procedures Manual 

Institute of Education Statistics (2007). 

http://nces.ed.gov/statprog/rudman 

This IES manual provides a guide to the restricted-use data application process, and to the 

laws and regulations governing these data. 

Managing Data Systems Security 

http://nces.ed.gov/whatsnew/conferences/mis/2009/session_v.asp#a 

New Hampshire Department of Education (2009).
 

This presentation briefly introduces New Hampshire’s education agency’s single sign-on 


system for user-identity management.
 

LDS Share 

The following documents are available at http://nces.ed.gov/programs/slds/ldsshare/slds.aspx: 

Compiled Data Access and Use Agreements 

Virginia Department of Education (2006).
 

This document from 2006 compiles data access and use agreements from Kansas, 


Oregon, Oklahoma, Illinois, Missouri, and Louisiana.
 

Policies and Procedures for Data Security and 


Confidentiality
 

Tennessee Department of Education (2005).
 

This sample shows how a state education agency documented policies and 


specific supporting procedures to guide governance related to data security and 


confidentiality.
 

Memorandum of Agreement Between Alaska Department of 

Education and Early Development and Computer Task Group 

Alaska Department of Education and Early Development (2006). 

Available via LDS Share at http://nces.ed.gov/programs/slds/ldsshare/slds.aspx 

This sample memorandum of agreement authorized a vendor's access to education 

data maintained and collected by the Alaska Department of Education and Early 

Development for the purpose of carrying out its contractual obligations related to 

the delivery of a portion of the agency’s data dictionary project. 

Book Three of Four: Effectively Managing LDS Data 95 

 

appendix D
 

handout: The ungoverned 

education agency 

The History and Consequences 

The state education agency’s data collection and management practices had come about over time, driven mainly by 

compliance and funding. Various program areas were created to focus on specific federal surveys, and staff collected the 

data needed to do their jobs. Program area staff administered the surveys, followed their own quality assurance processes, 

and maintained and secured the data in their own silo systems. And, of course, data were reported as required by the federal 

government. Individual managers took their own approach to directing staff and organizing work, and coordination across 

program areas was limited. 

Over time, different departments began to collect some of the same data elements. Inconsistencies were 

commonplace. For instance, while the student information system listed an “Aileen Hutchinson,” who was not in special 

education, the Special Education Department’s system included a girl named “Allie N Hutchinsen.” Despite these 

discrepancies, the staff knew the two records referred to the same student based on other directory information. However, 

structural differences between the systems—different definitions, formats, and option sets—further complicated matters. 

For example, Allie’s race, White, was coded as “1” in one database and “2” in the other because the systems used different 

option sets. Furthermore, because program areas defined their own data elements and used different software to manage 

them, the ability of the agency’s many data systems to “talk” to one another varied from limited to nonexistent, burdening 

staff with redundant data entry work and introducing errors into the system. With no clear requirement for documentation 

of data processes, methodologies often changed, as happened when Joe left the agency and no one else knew how to 

produce the dropout rate. The new guy, Steve, calculated it the way he had at his previous agency. No one saw a problem 

with this, especially since Steve’s numbers were lower than Joe’s. But when the time came to compare the new rates to 

previous year’s, some staff realized that they were comparing apples to oranges. 

Year in and year out, the work was done, but specific tasks weren’t assigned to anyone in a consistent manner, and 

sometimes reports were late or incomplete. And since there was no “official” source for each data element, data for federal 

reports might come from one source one year, another the following year. Similarly, without clear guidelines, staff fielded 

data requests as best they could. When inquiries came in, the recipient would decide to which program area the request 

should be sent. In one instance, Talia was asked for the school addresses in a district. While McKenna and Vita both 

managed school directory data, in separate systems, Vita was given the task because her desk was near Talia’s. 

When postsecondary education leaders asked about sharing data, agency officials cried “FERPA!,” invoking the 

federal privacy law they mistakenly thought prohibited such exchange (see chapter 7). Security and access protocols were not 

well understood, and staff often took a lax approach to protecting sensitive information (see “Identity theft in the printer 

room” in chapter 8). Data quality problems were handled as they arose, but no long-term changes were made to ensure the 

same issues wouldn’t crop up again. And since no responsible group or process was in place to identify the sources of the 

agency’s problems, they went undiscovered and data quality issues were just blamed on IT. This went on for some time and, 

other than the techies, most staff didn’t see much of a problem. It was simply business as usual. 

The Realization 

Over time, the country became more interested in education data. Education stakeholders wanted more information for 

accountability purposes and to better understand what programs and instructional strategies worked. They wanted data to 

inform decisionmaking at all levels; and to improve administration, instruction, and student performance. The bottom 

line was, they wanted data from across the agency and they wanted them fast. This changing environment posed many 

problems for the agency. Requested analyses required linking across silos, or data integration in a central data store. Before 

this could happen, duplicate, inconsistent data had to be reconciled. However, once the integration work began, more 

inconsistencies were discovered than anyone had imagined. Data quality had to be a higher priority, security had to be 

improved, and the data elements collected had to serve business and stakeholder needs, not just meet federal requirements. 

Book Three of Four: Effectively Managing LDS Data 97 
Better methods of sharing data had to be devised if the agency was to meet the growing demand for a “P–20” system. And 

better, more consistent protocols were needed to make data sharing more efficient and prevent improper dissemination. The 

chief information officer decided something had to be done. Having seen a presentation at a national conference, he was 

convinced a process called “data governance” could help address the agency’s problems. 

98 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
 

appendix e 

Data governance 

Supplementary materials 

Sample Data Governance Committee Mission 

Statement, Goals, and Objectives 

(Adapted with permission from Chatis Consulting) 

Example Data Governance Committee Mission 

The Data Governance Committee supports the _______ Department of Education’s 

mission of helping teachers teach and children learn by promoting the appropriate use of 

data to inform decisionmaking; and ensuring data quality, accountability, and timeliness. 

Examples of Data Governance Committee Goals 

	  Improve data quality.
 

	  Increase accountability for data accuracy.
 

	  Eliminate redundancy in data collection.
 

	  Improve understanding of data within the department and among districts.
 

	  Increase data use in program and policy decisions.
 

	  Improve capability and timeliness of data reporting.
 

Examples of Data Governance Committee Objectives 

	  Identify the owner of every data element.
 

	  Define all data elements.
 

	  Document all data processes.
 

	  Standardize data processes from year to year.
 

	  Reduce manual manipulation of data.
 

	  Identify the official source of data for all external reporting.
 

 Eliminate redundant data collections that are not the official source for external 


reporting. 

	  Allow districts to review their data before they are reported externally. 

	  Communicate all data decisions/changes to districts. 

	  Reduce the collection of, and reliance on, aggregate data. 

	  Increase the use of student-level data for external reporting. 

Book Three of Four: Effectively Managing LDS Data 99 
 

 

 

 

 

 

 

 

Sample Data Stewards Working Group Guidelines 

(Adapted with permission from Chatis Consulting) 

Process for addressing critical data issues 

1. Identify the data steward responsible for the issue and its resolution (one person). 

2. Determine whether a small working group of relevant data stewards should be created to 

address the issue. 

a. Does the issue directly affect the data quality or work of more than one program/ 

subject area in the organization? 

b. Note: Even if a working group is formed, only one data steward should be 

accountable for the issue. 

3. Plan first meeting of the working group. 

a. Clearly define (and document) the source of the problem—not the symptoms. 

This includes all aspects of the issue: communication (internal and external), 

definitions, technology, etc. A reporting problem is almost never just a reporting 

problem: its source is earlier in the data process. (Note: If the issue is complex, 

additional research and time could be require to fully identify it; this time spent 

at the beginning of the process is well worth it to fully understand what you are 

trying to address.) 

b. Determine the goals of addressing the problem; what exactly does the group 

want to achieve? (These goals should be aligned with the Data Governance 

Committee's goals). 

4. Create a mini-project plan for addressing each aspect of the problem and for achieving 

the established goals. 

a. Include main steps, including due dates and who must be involved in, or 

responsible for, each. 

b. Assign action items at the end of each workgroup session, including responsible 

staff member and due date. 

c. Determine whether any part of the issue is not within the purview of the Data 

Governance Committee. If so, the responsible data steward should bring the issue 

to the Data Governance Committee chair. 

d. Provide monthly updates to the Data Governance Committee for inclusion on 

the critical-data issues log. 

5. Once a preliminary “business” solution has been developed (i.e., you know what you 

want to do), coordinate with technology staff to get their input and determine how it 

can be implemented. 

6. Document all decisions made and implemented solutions thoroughly, and save to a 

common online area accessible by all Data Governance Committee members. 

100 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
 

 

 

 

7. Communicate these decisions and implemented solutions to the Data Governance 

Committee, all applicable program areas, LEAs, and any other staff directly affected by 

the issue. Be especially clear if the solution requires certain staff members to change how 

they work. 

8. Retire the issue from the critical data issues log, celebrate! Then move on to the next issue. 

Sample Critical Data Issues Log 

Data governance committee: Critical data issues 

Criteria: Creates a burden for the districts, causes errors or delays in federal reporting, or prevents the use of the 

data for its intended purpose. 

Retired data issues 

Source: Adapted from the Tennessee Department of Education’s Data Management Committee Critical Data Issues 

Log. Available via LDS Share at http://nces.ed.gov/programs/slds/ldsshare/slds.aspx. 

Data Issue Data Recent Status Action 

item description stewards(s) Priority action taken? update items 

Data Issue Data Date Last status Reason 

item description stewards(s) Priority retired update retired 

Book Three of Four: Effectively Managing LDS Data 101 

appendix F 

Forum and other nCeS 

resources 

Crisis Data Management: A Forum Guide to Collecting and 

Managing Data about Displaced Students (NFES 2010–804) 

http://nces.ed.gov/forum/pub_ 2010804.asp 

This publication provides guidelines that can be used by elementary 

and secondary education agencies to establish policies and procedures 

for collecting and managing education data before, during, and after a 

crisis. 

The Forum Guide to Data Ethics (NFES 2010–801) 

http://nces.ed.gov/forum/pub_2010801.asp 

Every day, educators collect and use data about students, staff, and 

schools. Some of these data originate in individual student and staff 

records that are confidential or otherwise sensitive. And even those 

data that are a matter of public record, such as aggregate school 

enrollment, need to be accessed, presented, and used in an ethically 

responsible manner. While laws set the legal parameters that govern 

data use, ethics establish fundamental principles of “right and 

wrong” that are critical to the appropriate management and use of education data in the 

technology age. This guide reflects the experience and judgment of experienced data 

managers; while there is no mandate to follow these principles, the authors hope that the 

contents will prove a useful reference to others in their work. 

The Forum Guide to Metadata: The Meaning Behind Education 

Data (NFES 2009–805) 

http://nces.ed.gov/forum/pub_ 2009805.asp 

This guide offers best practice concepts, definitions, 

implementation strategies, and templates/tools for an audience of 

data, technology, and program staff in state and local education 

agencies. This resource was developed to improve these audiences’ 

awareness and understanding of metadata and, subsequently, the 

quality of the data in the systems they maintain. 

Book Three of Four: Effectively Managing LDS Data 103 
Every School Day Counts: Forum Guide to Collecting and 

Using Attendance Data (NFES 2009–804) 

http://nces.ed.gov/forum/pub_ 2009804.asp 

This guide offers best practice suggestions on collecting and using 

student attendance data to improve performance. It includes a 

standard set of codes to make attendance data comparable across 

districts and states. The publication includes real-life examples of how 

attendance information has been used by school districts. 

NCES Handbooks and NCES Handbooks Online 

http://www.nces.ed.gov/programs/handbook 

The NCES Handbooks are a valuable source of metadata

for organizations and individuals interested in education data. These print and online 

resources define standard education terms for students, staff, schools, local education 

agencies (LEA), intermediate education agencies, and state education agencies (SEA). The 

Handbooks are intended as reference documents for public and private organizations, 

including education institutions and early childhood centers; as well as education 

researchers and other users of education data. In order to improve access to this valuable 

resource, NCES has also developed the NCES Handbooks Online, a web-based tool that 

allows users to view and download information via an electronic table of contents, a drilldown finder, element name and first letter searches, and advanced query options. 

National Education Data Model 

http://nces.ed.gov/forum/datamodel/index.aspx 

The National Education Data Model (NEDM) is the first 

non-proprietary, national education data model developed to help schools, LEAs, and 

states design or guide the selection of systems for instructional delivery, data-driven 

decisionmaking, data collection, operations, and reporting. The model provides a national 

blueprint to help schools evaluate and improve instructional tools; communicate needs to 

their umbrella agency or to vendors; enhance the movement of student information from 

one LEA to another; and, in the end, have better tools to inform instruction. NEDM can 

be used by educators, vendors, and researchers to understand the information required for 

teaching, learning, and administrative systems. 

Managing an Identity Crisis: Forum Guide to Implementing New 

Federal Race and Ethnicity Categories (NFES 2008–802) 

http://nces.ed.gov/forum/pub_ 2008802.asp 

This best-practice guide was developed to help state and local 

education agencies implement the new federal race and ethnicity 

categories, thereby reducing redundant efforts within and across states, 

improving data comparability, and minimizing reporting burden. 

Users may select and adopt strategies that will help them quickly begin 

the process of implementation in their agencies. 

104 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Forum Guide to Core Finance Data Elements (NFES 2007–801) 

http://nces.ed.gov/forum/pub_ 2007801.asp 

This publication establishes current and consistent terms and 

definitions for maintaining, collecting, reporting, and exchanging 

comparable information related to education finances. It is designed 

to accompany Financial Accounting for Local and State School 

Systems: 2003 Edition by identifying common reporting requirements 

and defining frequently used indicators and calculations that use data 

elements from accounting and other data systems. 

Forum Curriculum for Improving Education Data: A Resource for 

Local Education Agencies (NFES 2007–808) 

http://nces.ed.gov/forum/pub_ 2007808.asp 

This resource supports efforts to improve the quality of education 

data by serving as training materials for K–12 school and district staff. 

It provides lesson plans, instructional handouts, and other resources; 

and presents concepts necessary to help schools develop a culture for 

improving data quality. 

Forum Guide to Decision Support Systems: A Resource for 

Educators (NFES 2006–807) 

http://nces.ed.gov/forum/pub_ 2006807.asp 

This Forum guide was developed to help the education community 

better understand what decision support systems are, how they are 

configured, how they operate, and how they might be developed and 

implemented in an education setting. 

Forum Guide to Virtual Education (NFES 2006-803)
 

http://nces.ed.gov/forum/pub_ 2006803.asp 

This publication offers recommendations for collecting accurate, 

comparable, and useful data about virtual education in elementary and 

secondary education settings. It highlights policy questions and data 

elements critical to meeting the information needs of policymakers, 

administrators, instructors, and parents of students involved in virtual 

education. 

Book Three of Four: Effectively Managing LDS Data 105 
Forum Guide to the Privacy of Student Information: 

A Resource for Schools (NFES 2006–805) 

http://nces.ed.gov/forum/pub_ 2006805.asp 

This publication was written to help school and local education agency 

staff better understand and apply the Family Educational Rights and 

Privacy Act (FERPA), a federal law that protects the privacy interests 

of parents and students with respect to information maintained in 

student education records. It defines terms such as “education records” 

and “directory information,” and offers guidance for developing 

appropriate privacy policies and information disclosure procedures 

related to military recruiting, parental rights and annual notification, videotaping, online 

information, media releases, surveillance cameras, and health-related information. 

Accounting for Every Student: A Taxonomy for Standard Student 

Exit Codes (NFES 2006–804) 

http://nces.ed.gov/forum/pub_ 2006804.asp 

This publication was developed to help education agencies develop 

effective information systems for tracking students’ enrollment status. 

It presents a student-level exit code taxonomy for states and districts 

that accounts for 100 percent (not 90 or 110 percent) of all students. It 

also offers “best practice” advice regarding tracking students, collecting 

exit codes data, and distinguishing among high school completion 

credentials. 

Forum Guide to Education Indicators (NFES 2005–802) 

http://nces.ed.gov/forum/pub_ 2005802.asp 

This publication provides encyclopedia-type entries for 44 commonly 

used education indicators. Each indicator entry includes a definition, 

recommended uses, caveats and cautions, related policy questions, data 

element components, a formula, commonly reported subgroups, and 

display suggestions. The document will help readers better understand 

how to appropriately develop, apply, and interpret commonly used 

education indicators. 

Forum Guide to Building a Culture of Quality Data 

(NFES 2005–801) 

http://nces.ed.gov/forum/pub_ 2005801.asp 

This publication focuses on data entry: getting things done right at the 

source. It recommends a practical process for developing a “Culture 

of Quality Data” based around individual tip sheets for individuals 

involved in providing data, including principals, teachers, office staff, 

school board members, superintendents, data stewards, and technology 

staff. 

106 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Forum Unified Education Technology Suite (2005)
 

http://nces.ed.gov/forum/pub_tech_suite.asp 

This publication presents a practical, comprehensive, and tested 

approach to assessing, acquiring, instituting, managing, securing, 

and using technology in education settings. It is written for 

individuals without extensive experience with technology who have been tasked with 

leading technology initiatives in a school or district setting,. 

Forum Guide to Protecting the Privacy of Student Information: 

State and Local Education Agencies (NCES 2004–330) 

http://nces.ed.gov/forum/pub_ 2004330.asp 

This publication presents a general overview of privacy laws and 

professional practices that apply to information collected for, and 

maintained in, student records. The guide provides an overview of key 

principles and concepts governing student privacy; summarizes federal 

privacy laws; identifies issues concerning the release of information 

to both parents and external organizations; and suggests good data 

management practices for schools, districts, and state education 

agencies. 

Facilities Information Management: A Guide for State and Local 

Education Agencies (NCES 2003–400) 

http://nces.ed.gov/forum/pub_ 2003400.asp 

This publication provides a framework for identifying a basic set 

of school facilities data elements and definitions that will meet the 

information needs of school and community decisionmakers, school 

facility managers, and the general public. It presents recommendations 

for designing and maintaining an information system that addresses the 

condition, design, use, management, and financing of elementary and 

secondary education facilities. Commonly used measures, data elements, 

and additional resources for the practitioner are also included. 

Planning Guide for Maintaining School Facilities 

(NCES 2003–347) 

http://nces.ed.gov/forum/pub_ 2003347.asp 

This publication is intended to help school facilities managers plan 

for efficient and effective operations. It provides practical advice on 

a range of topics, including how to conduct a facilities audit, plan for 

maintenance to ensure smooth operations and avoid costly surprises, 

manage staff and contractors, and evaluate maintenance efforts. 

Book Three of Four: Effectively Managing LDS Data 107